We evaluate the sustainability of biorefinery supply chains by an input-output model.
We reveal the role of decision variables in supply chain analysis via sensitivity analysis.
We empirically demonstrate the model in a thistle-based biorefinery case in Italy.
Biorefinery is planned to produce bio-monomers, bio-lubricants, and bioenergy.
We assess the competitiveness of biorefinery products with their fossil-based correspondents.
This paper aims at evaluating the environmental and economic sustainability of bio-refineries that produce multiple products through their supply chains (SCs). A physical enterprise input-output (EIO) model is used to quantify the material/energy/waste flows and integrated to the monetary EIO model to compute the economic performance of bio-refinery SC (BRSC). The empirical case study is based on a (under-construction) bio-refinery which uses thistle oil and residues to produce bio-monomers, bio-lubricants, glycerine, and thermal energy in Porto Torres industrial district, Sardinia (Italy). Given the impact of uncertainty on the performance of the BRSC, we apply sensitivity analysis on the spatial, logistical, and biomass quality variables, i.e., land productivity, transportation distance, and thistle oil content rate.
In terms of practical contribution, the physical and monetary EIO models serve as planning and accounting tools for the involved companies of the BRSC. Findings show that the proposed models are effective in evaluating the sustainability of BRSCs and the investigated variables may significantly influence the economic viability of the bio-refinery. From managerial perspective, pricing contracts between the thistle producers and the bio-refinery is critically driven by the transportation distance. The bio-refinery can produce economically competitive outputs with an important contribution to the region’s employment market.
- Enterprise input-output analysis;
- Bio-refinery supply chain;
- Environmental and economic sustainability
In the recent, the development of bio-based industry has accelerated in Europe thanks to the EU’s circular economy policy which gives impetus to the economy-wise adaptation of sustainability through new business models. Hence, EU supports and encourages the (re-)use of bio-resources to create value-added with reduced environmental impacts in the bio-based industry . In this context, bio-refineries play a crucial role in processing bio-resources into materials, bio-products, feed, heat, and transportation fuels in an integrated production system. While the economic viability of second-generation bioenergy is still a challenge, bio-refineries offer to add value to biomass supply chains by producing bio-based chemicals (e.g. bio-monomers, bio-lubricants, glycerine, starches) .
The existence of multi-production pathways in bio-refinery supply chains (BRSC) raises some concerns such as the best-performance route selection and design, influenced by spatial (e.g., land dispersion, land productivity), logistical (e.g. energy density, transportation distance), and technological variables (e.g., mass recovery rate). Furthermore, in biomass supply chains, supplier-buyer relations are not easy to be stable because of seasonality, fluctuating harvest rates or biomass quality. Hence, farmers and biomass processing companies are usually hesitant for signing continuous supply contracts. However, as bio-refineries are able to recover secondary waste streams within their own processes, they are able to produce more value-added compared to one technology-based production. This allows bio-refineries to be more flexible with contracts compared to bioenergy companies. In addition, large-mass production in bio-refineries is still not available and the research is ongoing to overcome technological barriers. Process technology integration is a promising strategy to increase the economic sustainability of bio-refineries. As most bio-refineries are designed according to the availability of local resources, different combinations of process integrations are observed in different cases (e.g. Ref. . The empirical case of this paper deals with the use of thistle, which is a rich bio-resource that allows the producer to produce bio-products, namely bio-monomers, bio-lubricants, and glycerine (from thistle seed) and bio-energy (from thistle residues) in the Porto Torres industrial district, Sardinia (Italy). The bio-refinery is still under construction and expected to be operative by 2017.
This paper firstly aims at evaluating the environmental and economic sustainability of the thistle-based BRSC via enterprise input-output (EIO) modelling. Second, it assesses the effects of above-mentioned variables on the BRSC’s performance. Third, it provides managerial and practical contributions to the practitioners to better design BRSCs.
This paper analyses the economic viability and the environmental impacts of the abovementioned bio-refinery in Porto Torres, assessing its whole supply chain. In particular, we analyse the acceptability of the agreed biomass prices between the bio-refinery and Coldiretti (Italian Agricultural Entrepreneurs Association) via measuring total value-added and profit created by the BRSC. We further quantify the CO2 emissions along the BRSC. For both computations, we claim that the results will fluctuate according to three main variables, i.e., thistle productivity, transportation distance, and thistle oil content. Hence, we apply sensitivity analysis to these three variables and compare sustainability indicators and assess the competition power of bio-based products against their fossil-based counterparts.
We adopt a physical enterprise input-output (EIO) model to compute the material/energy/waste flows of the BRSC and integrate it to the monetary EIO model via cost/price vectors. Having the advantage of tracing inputs, outputs, and the secondary outputs, EIO approach is a suitable tool for evaluating the environmental and economic performance of BRSCs. The physical EIO model serves as a planning tool while the monetary EIO model serves as an accounting tool for the BRSC actors, i.e. farmers, third party logistics players, and the bio-refinery. The rationale of computing first the physical flows is to be able to calculate the technical coefficients among supply chain processes. This also allows us to compute several environmental performance indicators for different levels of production. Then, coefficient matrices are multiplied by unit cost/price vectors and monetary input-output table is computed. This serves as an accounting tool for measuring economic sustainability. Once the physical and monetary flows are computed, then we apply sensitivity analysis to three variables (i) land productivity, (ii) biomass transportation distance, and (iii) thistle oil content rate. For each scenario, we compute the economic and environmental performance indicators and discuss the results comparatively. CO2 emissions serve as environmental sustainability indicators while average prices of final outputs, total supply chain profit and value-added serve as economic sustainability indicators. Selection of sustainability indicators is based on the variables’ impacts on harvesting, transportation and processing phases which commonly contain energy consumption and CO2 emissions. Missing data do not allow us to apply neither LCA nor a comparison with a literature study as the use of thistle in bio-refineries has not been analysed at large-scale level. For economic sustainability, we particularly compute the average output prices to understand whether the bio-refinery products are market-competitive against their traditional counterparts. Generated profit is measured as a traditional economic performance indicator while the value-added is computed to evaluate the regional socio-economic contribution of the BRSC.
This paper is structured as follows: Section 2, drawing on literature, provides a review of the role of bio-refineries in implementing new business models, critical performance variables in BRSCs, and the use of input-output modelling for supply chain analysis. Section 3 describes the enterprise input-output (EIO) model. In Section 4, the case study is explained, proposed EIO model is applied to the case study and sensitivity analysis is performed. Results are discussed over sustainability indicators in Section 6. The paper is concluded by managerial and practical implications and contributions in Section 7.
2. Literature review
In developed countries, governments develop different strategies to improve the role of bio-refineries within bio-economy. Staffas et al. , review the efforts of most of the OECD countries comparatively. Their findings show that three major cross-cutting strategies exist for all countries investigated in the review: “(i) the balance between sustainability and economic aspirations, (ii) the limited attention to measuring progress, and (iii) the challenge of a limited supply of resources.” Indeed, the sector needs economic improvements as the versatile use of biomass does not allow to receive high amounts of supply from suppliers. Increasing the amount of supply would permit the companies to take advantage of the economies of scale and produce more market-competitive products. Hence, non-food local bio-resources, e.g. thistle, with low market competition are suitable for bio-refineries.
On the other hand, increasing the number of biomass suppliers and using more biomass in bio-refinery supply chains (BRSC) depend on the spatial dispersion of biomass, the quality of logistical infrastructure, and the advancements in processing technologies. Accordingly, the supply quantity is influenced by several variables causing uncertainty within BRSCs. Awudu and Zhang  discuss five types of uncertainties in biofuel supply chains which are adoptable also for BRSCs. These uncertainties are categorised as: (i) biomass supply, (ii) transportation and logistics, (iii) production and operation, (iv) demand and price, and (v) other uncertainties. Additionally, such uncertainties trigger each other and render the material planning harder for companies caused by less economic clarity. This motivates us to apply sensitivity analysis to three variables in our case study: (i) land productivity, (ii) biomass transportation distance, and (iii) thistle oil content rate.