Multi-level system modelling of the resource-food-bioenergy nexus in the Global South

Miao Guoa,*, Koen H. van Dama, , Noura Ouazzani Touhamib, Remy Nguyenb, Florent Delvalb, Craig Jamiesonc, and Nilay Shaha*

a Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UKb Energy Futures Lab, Imperial College London, South Kensington Campus, London, SW7 2AZ, UKc. Straw Innovations Ltd.

Abstract

To meet the demands for resources, food and energy, especially in fast developing countries in the Global South, new infrastructure investments, technologies and supply chains are required. It is essential to manage a transition that minimises the impacts on global environmental degradation while benefits local socio-economic development. Food-bioenergy integration optimising natural capital resources and considering wider environmental and socio-economic sustainability offers a way forward. This study presents an integrative approach enabling whole systems modelling to address the interlinkage and interaction of resource-food-bioenergy systems and optimise supply chains considering poly-centric decision spaces. Life cycle sustainability assessment, optimisation, agent-based modelling and simulation were coupled to build an integrated systems modelling framework applicable to the resource-food-bioenergy nexus. The model building blocks are described before their applications in three case studies addressing agricultural residues and macro-fungi in the Philippines, sugar cane biorefineries in South Africa, and Nipa palm biofuel in Thailand. Our case studies revealed the great potential of untapped biomass including agricultural waste and non-food biomass grown on marginal lands. Two value chain integration case studies – i.e. straw-fungi-energy in Philippines and sugar-energy in Africa – have been suggested as sustainable solutions to recover waste as value-added products to meet food and energy security. Case studies highlight how an integrative modelling framework can be applied to address multi-level questions, taking into account decision-making at different levels, which contribute to an overall sustainability goal.

Keywords: agent-based modelling, optimisation, bioenergy supply chain, LCA, Nipa palm, fungi

1. Introduction

This section introduces the concept of bioenergy-food-resource nexus, gives an overview of the bioenergy research in the Global South and reflects state-of-the-art in the field of bioenergy systems modelling.

1.1 Bioenergy-food–resource nexusExpanding populations and rapid urbanisation are leading to grand challenges at the nexus of resources, food and energy. Food and energy production are interconnected and complex, not only under increasing demand stress but also with land-water resource scarcity and environmental constraints. The projected 50% increase in global population in 21st century will lead to 11.2 billion by the end of the century [1]. Based on IEA report, this combined with the non-OECD economic growth is expected to lead to 33% increase in energy requirement [2]; meanwhile, as highlighted in the UK Government Office for Science over 50% rise in food demand globally can be expected by 2050 [3, 4]. Energy is the dominant greenhouse gas (GHG) contributor whereas bioenergy has been identified by IEA as one of the solutions for the low-carbon pathway towards the 2°C Scenario.

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Despite the fact that bioenergy has been widely recognized as a strategic component for energy decarbonisation, its development demands significant natural capital resource inputs (e.g. land and water) and is thus constrained by the environmental boundaries (e.g. arable lands availability) and conflicts with social-economic development (e.g. urban expansion) or other renewable energy investments (e.g. solar PV).

The agriculture sector is another significant consumer of natural capital resources, accounting for 37% of global lands and 70% of the water withdrawn [5]. By 2050, the agricultural water withdrawn of 3000 km3/yr and potential rain-fed land use (2.8 billion ha) and expansion of irrigated land use (0.33 billion ha) are expected to take place in water-scarce regions and developing countries [5]. This brings severe resource-competition issues in particular in the Global South, where food-bioenergy systems not only conflict and interact with each other, but also compete for resources with the municipal and industrial needs in response to rapid urbanisation trends and economic growth, which in turn lead to higher demands for energy, water and food. Under such a context, a pressing question is how food and energy demands can be met while achieving energy decarbonisation and the UN’s sustainable development goals (SDGs). Food-bioenergy integration optimising natural capital resources and considering wider environmental and socio-economic sustainability offers a way forward; thereby, a thorough understanding of the whole systems and involved issues and opportunities must be developed for the environmental, social and economic consequences of key decisions enabling the identification of sustainable pathways.

1.2 Bioenergy in the Global SouthBioenergy roles within the global energy portfolio are well reflected by regional and national energy policies e.g. the Renewable Energy Directive (RED) target of a 20% share of renewable energy in the EU energy mix by 2020 [6] and nationwide transport fuel targets mandated in the China 2020 policy. In contrast to other energy sources, biomass can be converted into solid, liquid and gaseous fuels used for heating, electrification, fuelling transport sectors. Globally, bioenergy sourced from various biomass including waste represents 14% of the primary energy mix [7] with approximately 2.6 billion people dependent on bioenergy consumption, but consumption patterns vary geographically. Currently, the total primary energy supply from biomass is 58 EJ where Asia accounted for half and the top 10 countries (China, India, Brazil, USA, Thailand etc.) shared nearly 40%. This observation highlights the spatial distribution of bioenergy and significant roles of the Global South. Countries in the Global South, especially African nations and South East Asian countries, have been identified as future growing regions with great biomass production potential on abandoned agricultural lands [8]. However, about 90% of the current bioenergy has been consumed in inefficient ways globally i.e. fuelwood, charcoal, agricultural residues for cooking and heating, which predominantly occur in Africa and Asia [9]. This use of bioenergy is not only inefficient, but also contributes to poor local air quality leading to respiratory diseases [10] and has a strong urban-rural divide (e.g.[11]). Despite the rapid growth of the bioenergy market, there are still large amount of untapped biomass resources including municipal solid waste and non-food plants, which represent the unexploited potential for bioenergy future [12]. The bioenergy sector is projected to remain a major contributor to the future global energy mix, where agricultural, forestry residues and charcoal are expected to be exploited more sustainably [9].

As summarised in Table 1, in addition to the environmental concerns, job availability in renewable energy sectors and energy accessibility and security are driving factors in Global South; these hinder the development of African and Asian continents which are experiencing energy poverty and food insecurity and are particularly vulnerable to environmental change threats. Currently, bioenergy supports nearly 2.9 million jobs globally, sharing 35% of employment in renewable energy sectors [7]; with the transformation of bioenergy sector from a currently inefficient system (90% traditional consumption) to a future technology-led system (e.g. value-added fuels), a rising job creation and shifting job market (towards skilful workers) can be anticipated. Biomass resources offer flexibility for energy generation, which can be either incorporated into the main generation systems (e.g. power plants) and utility grids or operated as distributed off-grid units in islanding mode.

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Table 1 – Overview of Global South regions

Africa Southeast Asia Population with no energy access

60% of 1 billion a 20% of 130 millionConstrains regional economy and quality of life

Fossil-driven electricity 2/3 of demand met by coal-dominated (90%) electricity

Driven by coal and heavy fuel oil

Environmental issues Deforestation Air quality Air quality Human health

Energy security Net fossil fuel importer, exposed to oil price volatilities

Rely on oil imports

Biomass usage Heating and cooking a Heating and cookinga. [9, 13]

1.3 Bioenergy systems and models To support effective strategies for bioenergy deployment and to evaluate a range of technology options and government policies, computational models can support decision-makers [14]. Such models can be developed specifically and tailored to particular products (such as biofuels, biogas or power), for particular crops, technologies, or for specific countries or regions. Questions to be answered with such models can relate to location choice, crop selection, technology sizing, demand forecasting, impact of global markets etc., but all rely on good system models describing the interrelation of different components. There have been increasing research interests in bioenergy system modelling where diverse approaches and paradigms have been pursued including environmental evaluation, agent based simulation and optimisation. Life cycle assessment (LCA) has been widely adopted to understand the environmental performances and further extended to sustainability assessment considering environmental and socio-economic performance; previous research on LCA of bioenergy systems has been covered in comprehensive reviews [15, 16]. Agent-based modelling (ABM) has been applied to analyse decision-making in bioenergy supply chains and predict decision-makers’ performances including agricultural sector and land use for bioenergy crop cultivation [17, 18]. By coupling ABM and LCA approach, recent studies considered the human behaviours, local variability in and scenario modelling in the bioenergy system. However, due to complexity and different nature of two models (non-linear computational ABM and linear deterministic LCA), it poses challenges on quantification of uncertainty and variability; a system approach was proposed to solve this challenge [19]. Mathematical optimisation was also widely developed to optimise the bioenergy refinery network and supply chains e.g. mixed integer linear programming (MILP) optimisation of hardwood-based biofuels [20]. Optimisation has been further integrated with continuous-time river basin model to explore the biophysical trade-offs caused by the bioenergy crop production where evolutionary algorithms were adopted to develop optimisation model [21]. In addition, optimisation model based on game theory was adopted to analyse the implications of decision-making e.g. policy on the bioenergy supply chain [22].

Despite the modelling advances in the bioenergy field, comparatively few studies considered bioenergy deployment options while simultaneously incorporating systems interaction or competition with non-energy systems (e.g. food) which are dependent on the same productive natural capital resources e.g. land, water. In particular land-use competition of food and bioenergy has been an emerging topic and addressed in few mathematical optimisation studies [23-25]. Previous research [26] adopted an ABM to model dynamic land use conflicts between food and biomass at spatial and multi-agent levels. ABM was applied in the study conducted by Guillem and Murray-Rust et al. [27]

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to understand the implications of farmer decision-making on trade-offs between food and bioenergy provisioning ecosystem services. Guo et al. [25] presented an MILP model to incorporate the resource competition on different land levels and wider ecosystem services into a spatial-temporal bioenergy system optimisation. Cobuloglu and Buyuktahtakin [28] proposed a two-stage stochastic MIP model to maximise the economic and environmental sustainability of food and biofuel production where decision-making under crop-yield and price uncertainties were investigated. Karabulut et al., [29] introduced resource-food-energy-ecosystem into LCA for synthesis matrix analyses. Cucek et al., [24] presented a MILP model for bioenergy network synthesis accounting for the food-fuel resource competition. A critical gap emerged on multi-level bioenergy-food system modelling in particular model development and applications in Global South [30]. These modelling approaches address specific elements of the bioenergy system decision-making at both strategic (e.g. location decisions and network design) and operational level (e.g. crop selection and control of conversion technologies), but using only a single approach means the interaction between these decisions is not fully taken into account. It should also be stressed that at the system level various stakeholders and decision-makers operate, each with their own decision space, access to information and a different weighting to key performance indicators, for example. By only using a single approach to analyse a subsystem, the impact on the wider system is not fully taken into account and sub-optimal recommendations might be made. This article addresses the research gap and presents an integrative approach enabling multi-level resource-bioenergy-food systems modelling to address resource-food-bioenergy nexus from a whole system perspective and consider poly-centric decision spaces.

This paper aims to build upon the advanced modelling approaches and proposes an integrative systems modelling framework (Section 2) for resource-bioenergy-food nexus planning in Global South, which is demonstrated via three design case studies under African and SEA country contexts (Section 3). Final conclusions are presented in Section 4.

2. Methodology Resource-food-bioenergy systems consist of several sub-systems across temporal/spatial scales including resources (e.g. land, biomass), food and bioenergy production, waste and pollutant fate and treatment, transport and network, market and demand. Across this complexity, different decision-making spaces are concerned at both systems (such as country-level) and local levels (e.g. individual decision-makers) for example - 1) resource supply decision in long/mid/short term; 2) long-term planning of resource transport and logistic network; 3) resource collection and flows in mid-term and short-term; 4) long-term decision on technology sizing, location of production site to convert resources into food and bioenergy; 5) mid-term decision on food/bioenergy production, inventory and flows; 6) short-term operation of process, storage and distribution network; 7) governmental policy and market regulatory decision; 8) financing decision. Thus under a multi-level decision-making system, multiple spatial and temporal scales need to be coordinated and multiple decision-space with conflicting criteria need to be addressed. This study presents a modelling framework to tackle such multi-level decision-making challenges in resource-food-bioenergy systems .

The modelling framework presented here (see Fig 1) consistent of three components - life cycle sustainability assessment (LCSA), optimisation and agent-based simulation. Combined these can provide valuable insights from both a whole systems perspective and local views, thereby covering the ranges from country-wide to local levels and inform decision-makers from governments to site operators.

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Figure 1 Modelling framework for resource-food-bioenergy nexus. Notes: S-LCA=social life cycle assessment; E-LCA=environmental life cycle assessment; LCC=life cycle costing; LCSA=life cycle sustainability assessment

2.1 Life cycle sustainability assessment LCSA refers to the evaluation of all environmental, social and economic impacts in decision-making processes towards more sustainable products throughout their life cycles [31]. Initiated from LCA techniques, the life cycle thinking approach has been extended since 2002 to form a LCSA methodology framework, which consists of three pillars - environmental LCA, life cycle costing (LCC) and social-LCA (SLCA) [31]. Under this framework, LCSA is adopted as a systematic and rigorous evaluation approach, providing holistic perspectives for multi-criteria decision on a given bioenergy system. As generalised in Eq. (1), LCSA accounts for all input-output flows occurring at each life cycle stage throughout the ‘cradle-to-grave’ of resource-food-bioenergy nexus. Formalised by the International Organization for Standardization [32], E-LCA quantifies the environmental footprints associated with all stages of a product or process. LCC and SLCA examine the economic aspects and social consequences respectively, evaluating the improvement opportunities.

E I kpi ,t=∑r∑

sEIf r ,kpi ,t

¿ F r , s ,t¿ X r , s ,t

¿ +∑c∑

sEIf c ,kpi , t

out Fc , s ,tout X c, s , t

out (1)

where the variable E I kpi ,t denotes the total impacts of a given food-bioenergy system (per functional unit) over time t expressed as key sustainability performance indicatorkpi. E I kpi ,tis determined by the characterisation impact factors for input resource r (EIf r ,kpi ,t

¿ ) or emitted compound c (EIf c , kpi ,tout ) and

the input-output flows (F r , s ,t¿ ∨Fc, s ,t

out ) and concentration (X r ,s , t¿ ∨X c , s ,t

out ¿at life cycle stage s. Bioenergy systems are characteristic of biogenic carbon cycles where biomass cultivation stage and carbon capture and storage technologies can act as carbon sinks, sequestering biogenic carbon from atmosphere into biomass and subsequently capturing CO2 emissions; biorefinery processes and use or disposal stages are carbon emitters, causing carbon release due to biochemical/thermochemical reactions or operational inputs. Handling such carbon circular complexity remains a challenge in particular for an allocation approach. For multiple-product systems, three allocation approaches are applicable to partition the material/energy flows and their associated sustainability impacts between

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the co-products i.e. allocation by physical relation (e.g. mass, volume, energy content, etc.), allocation by economic values or system boundary expansion [33, 34]. Under system boundary expansion, the avoided burden flows F r , s ,t

¿ F c, s ,tout are represented as negative values.

2.2 Centralised and decentralised optimisation

The supply chain (SC) optimisation problems concern the multi-site decisions on production, distribution, inventory and markets; multiple echelons are concerned in the SC design i.e. biomass cultivation, biomass and waste collection, pre-treatment and conversion, downstream distribution and storage. In the supply chain planning, decisions can be broadly categorised into three levels with respect to planning time horizon - 1) strategic decisions (long-term), 2) tactical planning on production, inventory and material flows (mid-term); 3) operational decisions (short-term) e.g. batch or continuous processing. The decision-making in a supply chain network can be performed in a centralised or decentralised manner; the former considers the entire supply chain as an entity system operated as a cooperative network, whereas the latter takes into account the competition and interaction of supply chain players so called agents, which are often non-cooperative. A number of bioenergy supply chain centralised optimisation have been published, which are summarised by the previous comprehensive reviews [35, 36].

Under this framework, supply chain optimisation is considered under both cooperative and competitive environments. Via incorporating resource vectors, a set of resource-competing bio-products, technology options, decision matrices and multi-agents involved in the supply chains, the optimisation model can be formulated to reflect supply chain network complexity and design space in food-bioenergy-resource nexus. The centralised SC optimisation problems can be generalised as Eq.(2), where MIP model is given to represent a typical cooperative SC network design.

Min ( f 1 ( x , y ) ,…. , f k (x , y ) )(2)s . t . h (x , y )=0

g ( x , y ) ≤0x∈ Rn

y∈ {0,1 }m

where K= {1,2 , … ..k }. Min( f 1 ( x , y ) , …. , f k ( x , y )) represents the objective function consisting of conflicting multi-objectives. Here x denotes the collection of continuous variables from each decision-maker or sub-systems, which are normally non-negative presenting the input-output material and energy flows as well as certain strategic or tactical decisions; discrete variables y define the selection of process units, network locations etc. and their interconnections at supply chain multi-echelons (e.g. biomass cultivation, biorefinery, distribution). Both continuous and discrete variables are bounded by the equality constraints h(x,y) e.g. material balance and inequality constraints g(x,y) and satisfy the design specifications (e.g. operational limits, environmental regulation threshold), and logical constraints. The derived set of non-dominated optimal solutions termed as Pareto front can be expressed as x¿∈X={x∈Rn|g ( x , y ) ≤0 ,h ( x , y )=0} where there exists no

x∈ X , f i ( x )|∀ i∈K ≤ f i ( x¿)∧f j (x )< f j(x¿). The set of Pareto optimal solutions are based on the trade-

off between conflicting objectives and can articulate the decision-makers preference in searching optimal solutions in the objective space. Decision-makers can be also involved prior to the optimisation (priori approach), where the multiple attributes are considered in the form of weighted sum in a single objective function such as the example given in Eq.(3); but the weight introduced for each criteria may change the dominance structure of the multi-objective problem.

min Objc=∑t∑kpi

(TI kpi ,tSC Weight F kpi ,t) (3)

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TI kpi ,tSC =CP I kpi , t+CA Pkpi ,t+OPEXkpi ,t+ RP I kpi ,t+ST ¿kpi ,t+RP I kpi ,t+TR I kpi ,t +WD I kpi ,t−CC Skpi ,t−CP I kpi , t

where weighting factor Weight Fkpi , t (e.g. market price for traded GHG emissions) is introduced for centralised supply chain optimisation problem. The variable TI kpi ,t

SC (Eq.(3)) represents the total impacts caused by the entire supply chain (SC) over time t expressed as key performance indicator kpi (e.g. economic and GHGs), consisting of the impacts caused by biomass cultivation (CP I kpi , t), capital input (CA Pkpi ,t), technology operation (O PEX kpi , t), imports (R I kpi ,t) and storage (ST ¿kpi , t), resource purchase (RP I kpi , t), transport (TR I kpi ,t), waste disposal (WD I kpi ,t), credits brought by carbon capture and storage/sequestration via technology or biological systems (CC Skpi , t) and the offset by co-products (CPIkpi , t) under expanded system boundary. LCSA approach in section 2.1 is applied to derive the impacts at various life cycle stages. A centralised supply chain optimisation model is given as an illustrative case study in section 3.3, where detailed formulations are given in Supplementary Information SI-3.

The decentralised optimisation problem can be defined as Eq. (4)

Min f i(x i)(4)s . t . hi

g (x i|{x j }i )=0

hil ( x i )=0

gig ( x i|{x j }i ) ≤0

gil ( x i )≤ 0x∈ Rn

where hi (x i|{x j }i ) and gi ( x i|{x j }i ) represent the functions of x i given that theith neighbour subsets {x j }i are fixed. x i corresponds to the collection of optimisation variables for the ith subsystem and follows both global interconnection constraints ¿ and gi

g) and local constraints ¿ and gil).

The SC decentralised optimisation involves multi-agents n∈N ={1,2 , …. m} coordination problems. To address the distribution of profits, resources and environmental responsibilities amongst agents, the algorithms based on game theory e.g. Nash equilibrium [37, 38] have been developed [39, 40]. Following the approach developed by [39], Eq.(5) presents the Nash-based MIP formulation proposed under this framework.

max Objd ¿∏n

¿¿¿

T I n=∑t

¿¿

where each agent n has a status quo point T̂I n reflecting an acceptable objective threshold; the optimisation solution for agent n denotes the total impacts T I n, where T I n≥ T̂I n. In the case of competition, a leader agent n has higher negotiation power (ε n¿ thus more advantages in profit allocation. The variable T I n, consists of the impacts expressed as performance indicator kpi throughout supply chain stages. Under decentralised decision-making, each agent n simultaneously optimises multi-scale decisions including the capital input (Ca pkpi ,n ,t ¿, financial decision (loan interest Interes tn , t ¿ and network locations at strategic planning level, tactical decisions on procurement, inventory and distribution (e.g. storage (ST ¿n , kpi ,t), resource purchase (RP I n , kpi ,t), transport (TT I n ,kpi , t) waste disposal (WD I n ,kpi , t ¿) and operational decision on trading (income SALE I n ,t, from trading in market and with other agents via supply chain transfer prices), profit sharing and policy instrument (SUBSID Y n , t∧TA X n ,t ¿. The policy decision variable

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SUBSIDY n , t∧TA X n ,t denote the agent n choice on governmental subsidy and taxation scheme respectively. Based on the IRN, IPCC, IE renewable deployment policy reports [41], key renewable deployment policies have been classified and generalised into seven schemes, which are presented in our published study [42].

2.3 Agent based modelling and simulation In agent-based modelling and simulation, a system is represented in a computational model building as a collection of agents within an environment. The agent perspective allows one to simulate individual actors in the system and their actions and interactions, ensuring heterogeneity and a natural representation. This modelling paradigm is particularly suitable for multi-actor and multi-level systems [43]. By representing an individual decision maker as an agent and capturing its behavioural rules in an algorithm with a set of rules, and allowing the agents to act on the environment and respond to other agents or external events, overall system level behaviour emerges [44, 45]. This way of modelling is called bottom-up, as opposed to top-down models where the overall system behaviour is described [46]. The agent-based modelling approach has been used to study the interaction of different actors in the supply chain since the early 2000s and is therefore not new, but it has been demonstrated to successfully represent key supply chain management phenomena such as the bullwhip effect [47]as a result of local decision-making and communication. Agent-based modelling is particularly suited to provide a natural representation of multi-level actors and their variable relationships, generating supply chain networks from individual actions rather than forcing a particular network structure. While the same supply chain can be modelled using more traditional equation-based models also, the flexibility offered by agent-based simulation allows the analysis of more flexible configurations [48]. Examples in the agrifood supply chain systematically reviewed in Utomo et al [49], comparing different agent classifications, decision-making rules, and types of agent decisions and interactions included in the models. This modelling paradigm has been used in a wide range of applications from finance to architecture and transportation to social science, see Macal [46]for an overview of typical disciplines and key references.

Supply chain management has been a key application domain given the nature of the system with multiple mutually dependent actors who each make their own decisions based on local conditions. Early examples and comparisons with alternative modelling approaches include bullwhip effects as an example of supply chain management [50-52]and evaluating policies to reduce this effect[53]. Recently, Moncada et al.[54] looked at policies for biodiesel supply chains, Huang et al.[55] examined farmer decision-making for bioenergy crop adoption and Yazan et al., [56] studied supply chains for manure-based biogas production using ABM. These applications show how individual actors in the supply chain can be simulated to generate system level outputs from changing configurations and the interactions between the agents, providing an environment to experiment with changes in behaviour and local optimisation as well as simulating dynamics in population, demand and supply. Each agent simulates a local decision maker which collects its own information from the environment (e.g. prices of feedstock on the world market) and other agents (e.g. availability of materials) with heterogeneous characteristics on objectives and decision space. This multi-stakeholder perspective suits the challenges presented in Section 1 particularly well.

To set up a model of the supply chain, relevant stakeholders can be incorporated. Below Table 2 gives examples of agents relevant to bioenergy supply chains and their behaviour rules that can be modelled. Such behaviour components can be combined and ideally configured in a way that meets the problem domain depending on the questions addressed by the model. Some models might include multiple instances of only one agent category, while others have a wider range of agent types, depending on the particular needs and data availability. The examples shown in Table 2 are provided here for inspiration for the kind of problems that can be addressed and support the value of this modelling approach in addressing real world problems related to bioenergy supply chains. An

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illustrative case study is given in section 3.2, where ABM is coupled with optimisation model; the detailed optimisation formulations are given in Supplementary Information SI-2.

Table 2 – Example agent types and behaviour rules to build bioenergy supply chain models

Agent type Example behaviour rules ReferenceFarmer What crops to grow, where to sell products [17]

[57]Logistics operator

Where to locate warehouse, which mode of transport to choose

[58]

Plant operator

Which feedstock to buy, what mode to operate the plant, what throughput to meet demand, how much to keep in storage

[59][56]

Investor Where to locate new facilities, what size) [60]Government What price level for subsidies, what taxes to

impose[54]

End user Which fuel or other products to purchase [61]Households How much demand for food, jobs, electricity,

water, transport fuels, etc.[62]

Water company

Water quality, water levels in reservoirs, operation of pumps and dams

[63]

Land owner Who to give access to use land in what way, what price

[64]

3. Results–case studies on resource-food-bioenergy nexusThis section presents three case studies on the resource-food-bioenergy nexus under a Global South context. Three case studies demonstrate the functionality of simulation-evaluation-optimisation tools and illustrate the integrative system modelling approaches from both bottom-up and top-down perspectives. The first case study addresses the environmental sustainability issues and explores agricultural residues in the Philippines for the co-production of edible fungi and energy; while the second looks at sugar cane supply chains in South Africa, simulating and optimising the local decisions for energy and sugar co-generation. The third case study from the centralised perspectives, optimises the biofuels from Nipa palm (Nypa Fruticans) in Thailand. All three case studies have their own local context, research objectives and different conversion technologies leading to diverse pathways and challenges in the food-energy nexus from different resources. However, they have in common their global south environment and a focus on SDGs, and show similar challenges in managing local decisions to meet national objectives using combinations of the modelling techniques described in section 2.

3.1 Nexus of agricultural residue, energy and edible fungi in the PhilippinesCompared with other SEA countries, Philippines represents an underdeveloped context with lower capita income and energy accessibility. Around 30% of the population has no access to electricity and 50% of local communities rely on biomass for cooking. This case study addresses the research questions on what would be the environmentally sustainable pathways to utilise agricultural residues for a resource-circular food-energy supply chain in SEA. To achieve this, an LCSA approach was adopted to investigate the environmental benefit potential and highlight the ‘hot-spots’ which deserve more empirical efforts for performance improvement. Rice straw, currently being treated as waste burnt in the open fields, has great potentials to be recovered as a resource; the potential pathways include plough-back as fertiliser [65], energy recovery from straw combustion [66], as feedstock for anaerobic digestion (AD), for animal feed and substrate for mushroom cultivation[67]. The edible macro-fungi growth (e.g. Pleurotus ostreatus, Lentinula edodes, Agaricus bisporus, Auricularia

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auricula-judae, Calocybe indica, Pleurotus citrinopileatus, Ganoderma lucidum, Hericium erinaceus, Pleurotus tuber-regium, Volvariela volvacea ) is of particular interests due to its added value and potential contribution to food security of the Philippines. Lately, Philippine government invested significant funds to support mushroom technology to substitute mushroom imports, which currently meet 90% of demands. Moreover, the spent mushroom compost (SMC) could be used as either animal feed (Anon, 2002) or feedstock for anaerobic digestion [68-71]. Our LCSA study examined an emerging supply chain integrating food-energy recovery from agricultural waste i.e. rice straw in comparison with two energy baseline scenarios (i.e. electricity generation from straw combustion and national grid electricity).

The LCSA was performed and implemented in the Simapro platform to model co-production of biogas and edible fungi from rice straw in the Philippines, where Pleurotus ostreatus cultivation acts as pretreatment for delignification and the SMC is used as AD feedstock for biogas generation. The functional unit was defined ‘per MJ electricity generated’ to evaluate the environmentally desirable pathways for power generation from straw. A problem oriented (midpoint) approach - CML 2 baseline 2000 was applied in the current study as the ‘default’ Life Cycle Impact Assessment (LCIA) characterisation method; and economic allocation approach was applied to allocate the environmental burdens based on the co-product market prices and quantity. The inventory was derived from primary data collected from site visits in the Philippines and an AD operational system [72], which were supplemented using secondary data from the literature. An example model output is given in Fig 2, where the characterised impact assessment are presented as normalised comparisons (%).The LCIA scores for each individual impact category are given in Supplementary Information (SI) Section SI-1, Table S-1. To illustrate the relative performance of the three power generation pathways (Fig 2), a normalized spider chart is presented in which the route with the largest occupied area represents an inferior system. The electricity generation from spent mushroom compost delivers clear environmental advantages over other two baseline i.e. electricity generation from combusting straw and the national grid electricity across all impact categories. In addition, the supply chain integration of edible fungi and bioenergy production also offers a potential solution to food security issues and green job creation locally, which are particularly important for countries in the Global South e.g. the Philippines. Our LCA combined with the social impact evaluation suggest that the straw-fungi-AD value chain integration represents a significant step forward towards our sustainable food-energy-resource nexus [73]. This research was built on site visit to International Rice Research Institute (IRRI) in Philippines. A recent study published by IRRI compared several options for rice straw management in Philippines including straw burning in the field, straw retained in the field, straw removal for mushroom production [74]. Future research should focus on the comparison of straw-fungi-AD with other straw value chains to advance the understanding of sustainable options for straw management.

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Figure 2 – Characterised LCA comparison of electricity generation (Method: CML baseline).

Notes: electricity generation route with the smallest occupied area represents a superior system; thus the spider chart shows that the electricity generation from spent mushroom compost delivers significantly beneficial environmental profiles in comparison with other two options.

3.2 South Africa sugarcane-food-energy nexus South Africa is the largest consumer of primary energy in Africa, representing 31% energy consumption and 40% African GHG emissions [75]. Despite the bioenergy potential to mitigate climate change, its production is challenged by the land and water availability at national level. Arable lands occupy 14% of the South African areas supporting food production and significant part of the nation face water stress due to semi-arid and arid climate [76].

This study aims to understand how a bioenergy supply chain can be integrated into existing food production facilities and supply chains in South Africa. A decision-support tool was developed based on ABM coupled with optimisation and applied to the sugarcane-food-bioenergy network planning at spatial and temporal scales in South Africa, where the LCSA thinking approach was adopted to account for entire life cycles of sugarcane based bioenergy. This demand-driven model takes into account the demographic dynamics and socio-economic factors, such as urban migrations and unemployment patterns and simulates the behaviours and interactions of multiple agents involved in the supply chain including sugarcane plantation, sugar mills, warehouses, distribution centres, consumers in urban areas and hinterlands, ports for international trading. In particular, the ABM simulation captures the change of different customer groups (city and rural areas) each year based on the demographic dynamics derived from South African statistics and World Bank data, leading to different demand patterns. By integrating the ABM-projected food and energy demands into the supply chain optimisation, several SC echelon design problems have been optimised. At the distribution echelon, the total profit is maximised, subject to the demand constraints where the supply meets demands for a given product but is upper-bounded by the distribution centre capacity. At the production echelon, total costs are minimised where the supply-demand equality and inequality

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constraints are considered. A flexible supply-distribution echelon structure was modelled in this study, where the creation of new distribution centres or biorefineries and operational flexibility of existing centres or biorefineries (upper-bounded by capacity) were enabled in the optimisation as a local decision, impacting the national objectives to create jobs and provide electricity from renewable sources. The ABM is implemented in the platform Repast, in Java. A case study on South African bioenergy-sugar co-production over the time period of 2015-2050 was modelled, where three biorefinery products were accounted for i.e. electricity, sugar and bioethanol and refinery agents had to make decisions on investments and operation. This combined agent classes from Table 2 such as households, investors/plant operators and logistics provider. The final optimisation results were exported and mapped out as a visualised supply chain design. Figure 3 is the output of the ABM and optimisation developed for the South African sugarcane supply chain. It shows an illustrative model output representing the graphical output of the model platform for a scenario modelled for 2050, where the optimal configuration of sugarcane plantation, biorefineries and distribution centres are mapped out. 2050 supply chain planning (Fig 3) concerns different decision spaces, where the multi-agents with decentralised economic objectives were modelled to design the logistics and locations for sugarcane plantations, biorefineries and distribution centres; 2050 scenario was bounded by the increasing demands as a consequence of growing population and urbanisation; the optimised land allocation for sugar cane cultivation are mainly distributed across four South African provinces (Mpumalanga, KwaZulu Natal, Eastern Cape, Free State) whereas the optimised biorefineries are located close to sugarcane cultivation sites. Model formulations are reported in supplementary information section SI-2.

In contrast to Nash equilibrium based SC optimisation presented in previous research [40, 77, 78]where the adjacent echelons and functional agents are defined a priori, this integrative modelling framework enabled the flexible echelon supply chain design. This study was based on site visits in South Africa hosted by Stellenbosch University. The detailed modelling approach and results are available in [79] and briefed in our previous conference proceeding [80]. By building the supply-demand complexity, this modelling toolkit can provide valuable insights for the decision-makers involved at each supply chain echelon to enable them to explore what-if scenarios with varying decision variables and under a range of socio-demographic scenarios.

This case study presents optimisation results derived from a central planning perspective with local decision-making by individual stakeholders in the sugar and bio-energy sectors. Alternatively, other value-added bioproducts can be derived from sugarcane through different biorefining technologies. A range of LCSA studies [81-83] have been published to compare sugarcane-sourced energy, biochemical, biofuel with baseline scenarios in South Africa such as sugar production and grid electricity generation (with rolling blackouts due to the lack of generation capacity and transmission network). These studies along with baseline (current sugar production and grid electricity) form the basis for future research on the integration and comparison of optimised food-bioenergy with other sugarcane value chain scenarios to explore optimal food-energy solutions in South Africa.

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Figure 3 – Optimal configuration of sugarcane plantation, biorefineries and distribution centres in 2050.

3.3 Emerging Nipa-food-energy nexus in ThailandBiofuel has been highlighted as part of the National Alternative Development Plan in Thailand, where the target production of bioethanol is set as 11.3 megaliters per day by 2036 [84]. This case study aims to configure new bioethanol supply chain solutions for an untapped food-energy resource i.e. Nipa in Thailand. Nipa palm (Nypa Fruticans) is a species of palm native to the coastline of the Indian and Pacific oceans. It grows abundantly in South East Asia and Africa and currently is distributed over Asia as either natural or plantation-managed resources for human consumption purpose (e.g. sugar alcohol, desert) [85, 86]. As an emerging untapped resource for biofuel, limited research has been published on its bioenergy potential [85], which revealed its desirable traits including fast growth, high-sugar content and high fermentation efficiency (9100L bioethanol/ha/yr in contrast to sugarcane of 3360-6700 L/ha/yr), non-restriction on seasonality and high water and nutrient efficiency. Amongst a range of traits, Nipa Palm is particularly featured for its resistance to saline water, which enables Nipa to grow on non-arable lands e.g. in abandoned shrimp ponds. The utilisation of Nipa for food production e.g. sugar or alcohol have been explored by local communities.

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The abandoned shrimp farms in Thailand offer great potentials to grow Nipa as a feedstock for bioethanol production.

By site-visits in Thailand and collaborative work with Prince of Songkla University, a MILP model has been developed, integrating with cost and environmental evaluation and considering the conflicting objectives i.e. the minimised biofuel SC costs and GHGs (Eq.(3)).The detailed formulations for MILP model are given in Supplementary Information SI-3. The carbon trading prices which vary with the temporal scales have been introduced in Eq.(3) as weighting factors (Weight Fkpi , t ¿to convert multi-objective optimisation into a weighted sum in a single objective function. The centralised optimisation solutions were derived for the spatially-explicit biofuel supply chain design over three decades (2010s-2030s) in Thailand, where biomass, biofuel production and distribution echelons were captured. Spatial distribution for Nipa plantation in coastal areas was estimated based on the research by Bamroongrugsa et al., [87-89], which lead to an average of 0.12% of coastal areas covered by Nipa palm. The Nipa yield potential (8.3-8.8 ton Nipa/ha/yr) and cultivation impacts were estimated based on field observation and data derived from previous research [85, 90]. The biorefinery performances were estimated based on [85, 91]. The carbon trading prices were assumed as £20, £25 £30 per ton CO2 for 2010s, 2020s, 2030s respectively. Different spatial scales were modelled in the scenarios i.e. national level vs. regional supply chain; a regional optimal SC configuration is given in Fig 4 as an example. Our results showed that the optimal cost profile is predominated (above 80%) by the biorefineries in the supply chain (unpublished work [92]). A local supply chain fulfilling the biofuel demands at coastal provinces in line with Thailand’s binding targets for transport sectors while preserving Nipa resources has been suggested as a more cost-effective solution. This case study optimised Nipa as an emerging resource for biofuel from a top-down centralised perspective; the allocation of profits and environmental duties amongst multiple agents and their local optimisation solutions can be further explored using decentralised optimisation algorithms, which will be explored in follow-up research. Following the national renewable energy development plan in Thailand, Nipa along with other biomass have been the research focus. The yield, chemical composition of Nipa sap may vary with plant variety, maturity, and plantation type, which further affect the biorefining process. Ongoing research is being carried out by researchers from Prince of Songkla University to advance the understanding of sustainability and impacts in response to commercialisation and expansion of Nipa-bioethanol production [87]. These efforts form good basis for future research on optimisation and comparison of future Nipa-food-energy systems against the baseline systems (i.e. current Nipa plantation and fossil fuel use) to inform the decision-making in Thailand.

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Figure 4 – Regional optimal supply chain configuration resulting from the MILP model.

4. ConclusionsBuilding on modelling advances on simulation-optimisation-evaluation, our research presents a multi-level systems modelling approach to inform decision-making on resource-bioenergy-food nexus planning in the Global South, where sustainable development is particularly of importance. Using three case studies, the capability of our methodology is tested in problem-solving under a developing region context. As highlighted in case study 1, LCSA offers a systematic and rigorous evaluation approach, providing holistic perspectives for multi-criteria decision on a given food-bioenergy nexus. Case studies 2 and 3 demonstrated the values of combining ABM and life cycle optimisation to address research questions from decentralised and centralised perspectives and account for multiple supply chain echelons with multi decision criteria. Reflecting on the model development within the proposed framework it can be summarised that implementing and validating optimisation models as well as agent-based simulation can be challenging and requires a specific skillset and access to data, while the life-cycle assessment and costing also depend on access to relevant databases and analysis tools with a steep learning curve which can be intimidating. It is therefore key to work with a team to address the kind of case studies presented in this paper, building on different individual strengths and experience. The models can be used independently as well as integrated to generate and combine insights and achieve a systems perspective, leading to more holistic decision-making. The combination, as well as the specific components in the proposed modelling framework therefore do not necessarily lead to more robust or stable solutions, but contribute to a realistic representation of the dynamics and complexity found in resource-food-bioenergy systems in particular, as evidenced by the case studies. This study aims to propose methodology framework, thus further research efforts will be placed on case study development and methodology demonstration. In particular an identified future research direction is to compare the presented multi-level systems modelling framework with alternative methodologies for food-energy value chain modelling. Another research frontier is to understand the effectiveness of the modelling on decision-support by contrasting the centralised and decentralised optimisation and comparing the optimal solutions against resource-food-energy baseline systems. Notably, generic decision criteria have been adopted in three case studies. However such global decision criteria are not necessarily applicable to address regional challenges in Global South. Multi-level system decision-making tools with region-specific data and sustainability criteria

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represents an emerging gap and future research direction, where heterogeneity and empirically based understanding of sustainable development in regional/national levels need to be considered [93, 94].

Despite the different biomass and conversion technologies involved in Southeast Asia and Africa, they share a common Global South environment with a strong focus on SDG goals i.e. climate action and clean energy. Our case studies revealed the great potential of untapped biomass resources including agricultural waste (e.g. sugarcane bagasse, rice straw) and non-food biomass grown on marginal lands (e.g. Nipa palm). This multi-level systems modelling framework enables cross-country learning of the food-bioenergy value chain integration solutions explored. In our research, two value chain integration case studies – i.e. straw-fungi-energy in Philippines and sugar-energy in Africa – have been suggested as sustainable solutions to recover agriculture waste as value-added products to meet food and energy security goals. The key barriers for the implementation of such a modelling approach in real-world problem-solving lies in the system complexity (such as the trade-offs between conflicting SDG objectives and interaction of multi-level decision-makers with their own objectives), uncertainty in natural and built environments, and the involvement of model users in interactive solution-searching settings to support informed decision-making. User interaction can be explored by developing a human-in-the-loop approach in multi-level modelling research to articulate the dynamic preferences of multiple decision-makers based on their gradually built understanding of the model topology and enable the solution search to be progressively directed towards the regions of interest.

Our future research aims to develop a suite of tools that are open and accessible to support grass-roots development of infrastructure plans, bottom-up decision making and collaborative design for resource-food-energy and beyond. As a next step, our research will focus on the model framework expansion to introduce resilience concepts in the model and integrate the stochastic programming, enabling the modelling of uncertainties in natural extreme events (e.g. flooding) and their implications on the resource supply demand and built infrastructures. The methodology will be further tested by implementing case studies in the Global South, e.g. resource-food-energy nexus in China. A cross-country learning model will then be explored by creating and managing repositories for input data and communication platform for practitioners to learn about decision-support models and applications. Overall, this paper presented the integrated approaches and illustrated their impacts with three case studies in the Global South. Our research highlights how an integrative approach by coupling life-cycle sustainability assessment, optimisation and agent-based simulation can be successfully used to address multi-level questions taking into account that decisions are made at different levels which all have to contribute to an overall sustainable and effective system.

5. Acknowledgement N.S. and M.G are grateful to UK Engineering and Physical Sciences Research Council (EPSRC) for providing financial support for the research project ‘Bioenergy value chains: Whole systems analysis and optimisation’ (grant reference: EP/K036734/1). M.G acknowledges UK EPSRC for providing financial support for her research through the EPSRC Fellowship project ‘Resilient and Sustainable Biorenewable Systems Engineering Model (ReSBio)’ (grant reference: EP/N034740/1). 

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