case studies research in the bioeconomy a systematic literature review

Case studies research in the bioeconomy: A systematic literature review

• Department of Agricultural and Food Economics
• Faculty of Agriculture, Food and Environmental Sciences
• Academic Field: Agricultural Economics and Appraisal
• Wageningen University & Research

Research output : Contribution to journal › Article › peer-review

• Circular bio-based economy
• Research methodology
• Resource management
• Sustainability

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• Research Psychology 100%
• Qualitative Psychology 66%
• Case Study Psychology 66%
• Labour Economics, Econometrics and Finance 66%
• Systematic Literature Review Psychology 33%
• Semantics Psychology 33%
• Empirical Research Computer Science 33%
• Recent Literature Computer Science 33%

T1 - Case studies research in the bioeconomy: A systematic literature review

AU - Tassinari, Gianmaria

AU - Drabik, Dušan

AU - Boccaletti, Stefano

AU - Soregaroli, Claudio

N2 - Case study research plays a crucial role in studying the development of the bioeconomy. The versatility of the empirical method coupled with the uncertainty surrounding the bioeconomy concept requires a consistent and comparable application of the method to obtain valid and generalizable results. To stimulate such systematization, we first need to know the state of case studies in bioeconomy research. This article reviews the recent literature with a qualitative content analysis facilitated by systematic text coding. Our results provide an overview of how the narratives of the concept of bioeconomy affect the versatility of the case study research. Based on the low density of the illustrated semantic networks, we conclude that future empirical research on bio-based phenomena should be more transdisci-plinary and rely more on cross-sectoral approaches. Further work is also required in developing common research protocols that support transparency and replicability of case studies in the bioeconomy.

AB - Case study research plays a crucial role in studying the development of the bioeconomy. The versatility of the empirical method coupled with the uncertainty surrounding the bioeconomy concept requires a consistent and comparable application of the method to obtain valid and generalizable results. To stimulate such systematization, we first need to know the state of case studies in bioeconomy research. This article reviews the recent literature with a qualitative content analysis facilitated by systematic text coding. Our results provide an overview of how the narratives of the concept of bioeconomy affect the versatility of the case study research. Based on the low density of the illustrated semantic networks, we conclude that future empirical research on bio-based phenomena should be more transdisci-plinary and rely more on cross-sectoral approaches. Further work is also required in developing common research protocols that support transparency and replicability of case studies in the bioeconomy.

KW - Circular bio-based economy

KW - Protocol

KW - Research methodology

KW - Resource management

KW - Sustainability

UR - http://hdl.handle.net/10807/188981

U2 - 10.17221/21/2021-AGRICECON

DO - 10.17221/21/2021-AGRICECON

M3 - Article

SN - 0139-570X

JO - ZEMEDELSKA EKONOMIKA

JF - ZEMEDELSKA EKONOMIKA

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Review article, logistics and supply chain modelling for the biobased economy: a systematic literature review and research agenda.

• 1 Wageningen Food and Biobased Research, Wageningen, Netherlands
• 2 Department of Business, Strategy and Political Sciences, USN School of Business, University of South-Eastern Norway, Kongsberg, Norway

One way to mitigate the negative impacts of climate change, is for society to move towards a biobased economy, where fossil resources are replaced by biobased ones. This replacement requires the development of biobased supply chains that differ significantly from the conventional supply chain. For example, seasonality and variability of the feedstocks create specific challenges for biobased systems and call for customized solutions for the design and operation of biobased chains. As a result, the modelling efforts to support decision-making processes for biobased logistics and supply chains have some different requirements. This paper presents a systematic literature review on logistics and supply chain modelling studies for the biobased economy published in a period of 2011–2020. The literature analysis shows that most modelling studies for the biobased economy are strategic optimization models aiming to minimize economic impact. As biomass source, forest and agricultural residues are mostly used, and fuel and energy are the most common biobased applications. Modelling strategies, biomass sources and applications are however diversifying, which is what we encourage for future research. Also, not only focusing on economic optimization but also optimizing social and environmental performance is an important future research direction, to deal with the sustainability challenges the world is facing.

1 Introduction

To avoid climate change by greenhouse gas emissions and its harmful consequences, our society needs to move away from the current fossil-based economy towards a biobased economy with biomass as a renewable resource ( Sanders et al., 2010 ). This requires the design of biomass supply chains at reasonable costs and with low environmental impacts, which are broadly accepted by society ( Nunes et al., 2020 ). In some regions, biobased supply chains are already established, such as the sugar cane to ethanol supply chain in Brazil and the palm oil supply chain in South East Asia ( Lewandowski, 2018 ). However, in other many regions and with other sources or applications, biobased supply chains are still under development. Taking a logistics and supply chain modelling perspective for the bioeconomy allows for a systems approach, and look beyond individual products or factories. Logistics and supply chain models that are suitable to help decision makers to design and set up new supply chains for specific biobased products will stimulate the biobased economy ( Atashbar et al., 2016 ).

Logistics and supply chain models for the biobased products are complex because of seasonal availability and variability of the biomass sources, scattered geographical distributions, quality variations, biomass deterioration, diverse conversion technologies and by-products, and inter-dependencies among logistics operations ( Caputo et al., 2005 ; You et al., 2012 ; Malladi and Sowlati, 2018 ). In order to support the decision-making, supply chain models for the biobased economy that have to deal with all these different aspects tend to become very complex.

Contrary to biofuel and bioenergy logistics and supply chain models, supply chain models that focus on other types of biobased products such as biochemicals and biomaterials have not been subject to review broadly yet. However, there have been some reviews on biorefinery supply chain models ( Pérez-Fortes et al., 2011 ; Wang et al., 2015 ; De Buck et al., 2020 ). Biorefinery supply chains are still studied in a fragmented and partial manner, and the complexity of biorefinery supply chains makes it hard to optimize them ( Pérez et al., 2017 ). The review of De Buck et al. (2020) focuses mainly on the processes within the biorefinery itself. Pérez et al. (2017) focus on sustainability, but not from a modelling perspective and Wang et al. (2015) focus on bioenergy production. Also, Bussemaker et al. (2017) note that biorefinery supply chain analysis has traditionally focused on biofuel or bioheat production and tools for the evaluation of non-biofuel products like biochemicals and biomaterials are increasingly needed.

This paper aims to review logistics and supply chain models for the biobased economy, for all biomass sources and all biobased products. For this purpose, we systematically selected studies on logistics and supply chain modelling for the biobased economy. They were categorized and analyzed based on their scope (supply chain perspective, decision level, biomass source and application), their modelling choices (modelling and solution approach) as well as their goal. This way, this study provides a detailed overview of developments in logistics and supply chain models for the biobased economy in the last decade. Moreover, this study aims to provide an analysis of the gaps in literature so that a future research agenda can be formulated.

The remainder of the article is structured as follows. Section 2 provides the reader with further theoretical background and concept definitions of biomass logistics and supply chains for the biobased economy. In Section 3 we discuss our methodology, in Section 4 we show and discuss the general results and the results for each section of the analytical framework. In Section 5 we draw conclusions and in Section 6 we suggest directions for future research.

2 Theoretical Background

2.1 concepts related to biomass logistics and supply chains for the biobased economy.

As biobased is often referred to as alternative to fossil based, we could state that it applies to all products can be made from fossil origin but can be replaced by organic resources. This means bio-based would include biochemicals, biomaterials, biofuels and bioenergy. The term “bioeconomy” is not necessarily focused on replacing fossil resources but it is more focused on the origin itself. Therefore it does not only entail bio-based but also food and animal feed (hereafter referred to as “feed”), which have always been of organic origin. This would thus make the bio-based economy a subset of the bioeconomy. Birner (2018) describes the replacement of fossil based resources by bio-based ones as an opportunity inherent in the concept of the bioeconomy. It should be noted however that the distinction between the bioeconomy and the biobased economy is not always clear, and can differ between sources and countries. Sometimes the terms are used interchangeably, for example in Lewandowski (2018) .

The biobased economy value chain is integrated with the food and feed production chain through biorefineries that use biomass to deliver biomass components that can be used to make biobased products. Biomass that is used as a feedstock is an organic substance of one of the following types: agricultural crops and residues including lignocellulosic crops and residues (wood, grasses, or non-edible parts of plants) and food crops, fresh biomass (such as grass) and aquatic biomass, such as algae and seaweed ( Sharma et al., 2018 ).

The International Energy Agency (IEA) Bioenergy Task 42 “Biorefining in the Circular Economy” defines biorefinery as the sustainable processing of biomass into a spectrum of marketable biobased products and bioenergy ( Lindorfer et al., 2019 ). In a biorefinery, biomass sources are pre-treated, and then isolation and extraction are performed of valuable biomass components such as carbohydrates, proteins, natural fibers, lignin, specialties and oil and fats that form the basis for further conversion into biobased products ( Yue et al., 2014 ). Various types of conversion can be distinguished: biotechnological and chemical conversion that can be combined with synthesis and modification and polymer processing. Groups of biobased products that can be distinguished are biochemicals, biomaterials, biofuels and bioenergy. Those biobased products can range from high-value, low volume fine chemicals such as pharmaceuticals, cosmetics, food additives to high-volume materials such as biofuels and fibers ( Langeveld et al., 2010 ). A schematic representation of a biomass supply chain for the biobased economy is shown in Figure 1 .

FIGURE 1 . Schematic representation of a biomass supply chain for the biobased economy.

2.2 Biobased Logistics Models

Like all mathematical models, biobased logistics models have a goal, or objective function. Often, this is an economic objective, but performance can also be expressed in another sustainability dimension with a social or an environmental objective.

We categorize the scope of the model in four aspects, namely supply chain perspective, decision level, biomass source and biobased application. The supply chain perspective refers to which part of the supply chain is modeled: does the model describe the whole chain, or a part of the chain? Does it have a circular perspective? Decision levels can be divided into strategic, tactical and operational levels. In supply chain management, strategic decisions include network design, facility planning, location planning, capacity decisions; tactical decisions include distribution planning, amount of flow, mode of transportation; and operational decisions include production scheduling, customer demands and pricing ( Kumar et al., 2020 ). The biobased source describes which source is used, and the application describes in which type of application the biobased source is being used after the biorefinery process.

In terms of modelling perspective, we consider three dimensions: the modelling choices (related to probability, time, and objectives), the solution approach (optimization, simulation, a combination thereof, game theory), and whether the modelers have chosen to incorporate modelling approaches from other disciplines. We will further elaborate on the solution approaches and the incorporation of modelling methods from other disciplines in Sections 2.3, 2.4.

2.3 Solution Approaches for Biobased Logistics Models

Mathematical models can be solved through optimization, simulation or a combination thereof ( Bierlaire, 2015 ). Mathematical optimization models are designed to find the optimal solution given a set of constraints and a goal function. For example, to find the optimal location of a biorefinery to minimize transportation costs given some geographical restrictions. Different methods exist to solve an optimization model, for example, rigorous optimization, fuzzy programming and machine learning.

Simulation models can be used to analyze “what happens if” type of questions, and to compare multiple scenarios with each other. For example, to compare the effect of different fuel demand scenarios on the operations of a biorefinery. Optimization and simulation can also be combined, for example to test the robustness of the solution provided by the optimization ( Kleijnen and Gaury, 2003 ).

2.4 Combination With Other Models and Computational Methods

In most studies, the inputs are given and the outputs are the main result of the model. Sometimes however, the inputs and output can also be subject to model calculations. In the context of biobased supply chains, we see the following models and computational methods being combined with optimization and simulation models: life cycle assessment (LCA), geo-information systems (GIS) and process simulation. LCA can be used to calculate environmental effects of a production chain. This can be done after the optimization, but it can also be part of the optimization, when the goal is, for example, to minimize environmental impact. GIS can be used to feed the mathematical model with geographical data, and it can be used to depict the suggested outcome on a map. Process simulation is used to estimate the parameters and effects of chemical processes ( Chaves et al., 2016 ).

Figure 2 summarizes the structural dimensions and related analytical categories used in this study.

FIGURE 2 . Biobased supply chain model analytical framework.

Table 3 shows the options considered for each of the dimensions of the framework.

3 Methodology

This study reviews the literature of logistics and supply chain modelling for the biobased economy to define the current status of the research in this field and to identify research gaps and future research directions. To this aim, we follow a structured process to ensure reproducibility and objectivity, inspired by the studies of Agi et al. (2020) and Wee and Banister (2016) :

- Literature delimitation: defining the search boundaries.

- Material collection: defining the search strategy for strategically selecting the papers. The outcome of this step is a database containing the list of papers to review (hereafter: “the database”).

- Descriptive analysis: describing formal aspects of the papers, such as the publication date and journal.

- Category selection: specifying the categories in which the papers will be classified.

- Material evaluation: discussing the papers and their specific characteristics. By analyzing the source materials, we will provide insights in on logistics and supply chain modelling literature and reveal promising future research directions.

3.1 Literature Delimitation

In this section, we describe the criteria used to define the search boundaries. This study reviews the literature on logistics and supply chain models for the biobased economy. Therefore, each article that is included in the analysis should be about biobased logistics or the biobased supply chain, and there should be a (quantitative/mathematical) model described in the study. Studies were selected from Scopus and Web of Science, published in English in peer-reviewed journals. Review papers were only used for the introduction of this study but not for the content analysis. Conference papers, book chapters and technical reports were also excluded. As we aimed to focus on supply chain models, studies were not considered for further analysis if their main aim was to generate data, for example, macroeconomic studies, life cycle assessment (LCA) studies and geo-information systems (GIS) models. If those models however were combined with, or used as an input for a supply chain model, they were considered for further analysis.

3.2 Material Collection and Refinement

In our survey of publications, we queried the Thomson Reuters bibliographic database Web of Science and Elsevier’s Scopus database since both are commonly used for bibliometric analysis, but they also differ substantially in coverage ( Mongeon and Paul-Hus, 2016 ). Moreover, we used backward and forward snowballing to extend our literature base with additional relevant papers.

We searched for the combination of specific keywords in the title, abstract, and author’s keywords. The set of keywords used includes:

- “logistic*” OR “supply chain”: to focus the search on supply chain/logistics studies;

- “biobased” OR “bio-based”: to focus the search on biobased applications;

- “model*”: to limit the results to studies containing a modelling approach;

- “biorefinery”: we noted that studies on biorefineries are often about the biobased supply chain, however they do not always use the word “biobased”. In order to not miss out on those publications, we included this search term.

The asterisk (*) is used to include different possible endings/spellings of a word, such as modelling, modeling, models.

Two keyword combinations were used:

[(biobased OR bio-based ) AND (logistic* OR (supply AND chain )) AND model*)], and [ (biorefinery AND model* AND (logistic* OR (“supply chain*”))].

The search was done for the past decade; so studies published between 2011 and 2020 were included.

All four co-authors of this study separately scored all studies article based on the title, abstract and keywords in the following manner:

• The article is about biobased or a biorefinery.

• The article is about supply chain or logistics.

• The article is based on a quantitative approach, a modelling component is considered.

- A medium score (0.5) was given when there was doubt if the above requirements were met.

- A low score of (0) was given when at least one of the requirements was not met.

The paper was only considered for further analysis when the summed score of the (4) authors was equal to or higher than 3. The initial search resulted in a significant paper database (436 studies), which reduced in size after duplication removal and scoring them on relevance (84 studies). Figure 3 shows the initial results as well as how the number of papers was reduced by duplication exclusion and scoring.

FIGURE 3 . Literature search and refinement scheme.

4 Literature Analysis

In this Section 4, we first provide an overall description of our literature review database. Then, we provide the reader with a literature analysis per the analytical framework category. The final literature review database contained 84 papers. Table 1 gives the overview of the journals in which the studies were published and Table 2 shows the years of publication. The 84 papers were published in 23 different journals. Journal of Cleaner Production was found as the most common outlet for this research topic, followed by Applied Energy. Of the studies in the literature database, 38% were published between 2011 and 2015, and 62% was published between 2016 and 2020.

TABLE 1 . Journals in which the articles of the final review database were published.

TABLE 2 . Years in which the papers of the final review database were published.

Table 3 summarizes the categories used to score and analyze the studies in the database.

TABLE 3 . Categories used to score the articles in the database.

For each category, we give a short explanation and the main findings related to that category. A summary of scoring results is given in Table 4 and the complete scoring results are given in Supplementary Table S1 .

TABLE 4 . Summary of the results.

4.1 Biomass Source

The following biomass source types were found in the studies: forest/forest residues, agricultural residues, food waste or side-streams, energy crops, the biological fraction of municipal solid waste (MSW) and biomass of aquatic origin. Figure 4 gives an overview of all biomass sources in a pie chart. Some studies did not specify which biomass source was used, often they just used “biomass” to describe it. In Figure 4 , those studies are referred to with “not specified.”’

FIGURE 4 . Biomass sources studied in the models in the database.

The biomass sources agricultural residues (35%), forest residues (27%) and energy crops (24%) were most prevalent. The results change over the years: up to 2013, these three were the only sources described in the studies, and from 2014, also municipal solid waste (MSW), food waste, biomass of aquatic origin are mentioned as biomass sources.

4.2 Biobased Products

As discussed in Section 2, we define the biobased products: chemicals, fertilizer, materials, energy and fuel. However, since there is no universally agreed upon definition, and some studies also consider food and feed as part of the biobased products, we also consider those applications in our studies.

Figure 5 shows that fuel and energy (76%) is the most common application area. The other application areas (chemicals, fertilizers, materials, feed and food) sum up to 24%, and have mainly started to show up in studies published in or after 2018.

FIGURE 5 . Product (application area) of the models in the database.

In Section 2 we discussed that bioeconomy and biobased are sometimes used interchangeably, and we argued that biobased could be considered an alternative to fossil based while bioeconomy entails everything with of organic origin (applications: biobased and food and feed). Our results showed only three studies that include food and feed applications came up in the search results showing that either most studies also use this definition or that food and feed studies are a longstanding subject of its own and are therefore not marked as biobased.

4.3 Supply Chain and Logistics Perspective

For each study, we checked if it focused on the whole supply chain, part of the supply chain, and/or if it had a circular perspective. Finally, we checked if collaboration or supply chain integration was discussed.

About two-third studies focused on the whole supply chain (69%) and about one-third (31%) focused on a specific part of the supply chain. Only three studies considered a circular perspective. Of all studies, 13% consider supply chain integration and/or collaboration. This is a low percentage since biorefineries can process streams from multiple actors and for multiple markets ( Guo et al., 2020 ), and logistics collaboration can make it easier to achieve sustainability improvements ( Stellingwerf et al., 2018 ).

4.4 Decision Level

We found most papers use a strategic decision level (59%), some approach the problem from a tactical decision level (30%) and a small group chooses an operational decision level (11%). The majority of the studies takes a strategic approach, which links to the majority of the studies taking a whole chain perspective.

Some studies (11) combine decision levels. Alizadeh et al. (2019) are the only authors combining all three decision levels. Most others (8) combine strategic and tactical decision levels, and two studies ( Geraili et al., 2016 ; Gargalo et al., 2017 ) combine strategic and operational decisions levels.

4.5 Modelling Method and Solution Approach

For the modelling methods classification, we checked which choices were made in terms of probability, time and goal of the model. In terms of probability, we found that 67% of the models uses a deterministic approach, 10% use a stochastic approach, and 23% combines both approaches. In terms of time, 25% uses a single-period model and 75% uses a multi-period model. In terms of objective function, 70% uses a single objective, and 30% uses a multi-objective approach. Section 4.6 describes specifically which objective functions were used.

Thus, in terms of probability, deterministic models are most common, in terms of time, a multi-period approach is most common, and in terms of goal, the single objective is most often used. The multi-period aspects of the models can be linked to the seasonal nature of biobased supply chains. Despite the fact that deterministic models are most common within this selection of studies, there is an increase of the number of stochastic models over the decade.

The solution approaches found can be grouped into optimization, simulation, a combination of optimization and simulation, and game theory. Next to that, part of the studies use combined methods in which either heuristics or simulation or both are combined with GIS, LCA, or process simulation. Note that, in terms of solution methods, with simulation, we mean event-driven simulation. While in the combined methods, we check for process simulation which is a method used in chemical engineering to help with analysis, design and optimization of chemical processes ( Chaves et al., 2016 ). Figure 6 depicts the division of those methods among the studies.

FIGURE 6 . Objectives used in the studies.

Figure 6 shows that optimization is most frequently used (75%, 64 studies), followed by a combination of optimization and simulation (18%); simulation is used in 6% of the cases, and 1% of the studies (1 study) used game theory.

Of the studies that used optimization, most used rigorous optimization; 8 used heuristics techniques to get to a solution within an acceptable computation time, 3 used fuzzy programming, and 1 used machine learning. Note that half of the studies using heuristics appeared in 2019 and 2020, so that seems a recent development.

Of the 84 studies that used optimization, simulation, or a combination of both, 33 studies combined this with LCA, GIS or process simulation. Table 5 shows which combinations of solution methods and computational models were used.

TABLE 5 . Overview of the number of studies combining solution approach with another computational model.

GIS was used in 17 studies, LCA in 11 studies, and process simulation in seven studies.

4.6 Modelling Goal/Sustainability Approach

The objective functions found can be grouped into economic, environmental and social objectives, and into a combination of those. Figure 8 shows the division of the different objective functions over the studied papers.

Figure 6 shows that most studies focus on a single economic objective (72%). This is an interesting observation since almost all of the studies give environment-related reasons as a justification for executing their study. 16% of the studies combined economic and environmental objectives and 11% had objectives in the areas of all three sustainability pillars: people, planet and profit, for example, Chávez et al. (2017) . Over time, there is no clear trend in terms of objective functions.

4.7 Geographical Region

For all studies we checked if they had a case study, and if the authors described where the case study was executed or on which geographical region the data were based. Most studies (63 out of the 84) described the case study location, the others did not describe their location, did not have a case study, or their case study was based on a hypothetical example. Figure 7 shows the division of case studies according to continents.

FIGURE 7 . Solution approaches used in the studies.

Figure 8 shows that most studies (62%) were performed in North America, followed by Europe (15%), South America (11%), Asia (10%) and Africa (2%). The case studies are not evenly distributed geographically. This is striking since the biobased economy is something that is relevant to the whole world.

FIGURE 8 . Geographical region of the case study (continent).

Since most studies were performed in North America, we checked if there were differences between the studies that were performed there, and the studies that were performed in the rest of the world. We took the rest of the world together since we only have a few of those in our database, so if we grouped them per continent, we would not be able to make statements about them. The results are shown in Table 6 .

TABLE 6 . Cross-categoric analysis between geographical location and the other categories.

Most categories do not show much differences between North America and the rest of the world. However, the following areas show interesting differences. Studies in North America focus less on using agricultural residues compared to the rest of the world, and less often specify the type of biomass used. Also, all studies using biomass of aquatic origin were performed outside of North America.

Outside of North America, collaboration or integration was studied relatively more often. In terms of solution approach, it is striking that optimization was used in all of the studies outside of North America (sometimes combined with simulation), and in North America, simulation was applied more often. In terms of combination with other computational methods, GIS is applied a lot more often in North America, while LCA is more common in the rest of the world. Both North America and the rest of the world focus on economic objectives, but studies performed in the rest of the world more often also used environmental and social objectives.

5 Discussion and Conclusion

5.1 summary of main findings.

In this study, we have collected, selected, and analyzed studies in the last decade on logistics and supply chain models for the biobased economy. We have found that most models are optimization models aimed at minimizing economic impact. Some more recent studies also combine the optimization methods with other methods such as LCA, GIS and process simulation–primarily used to obtain input data for the optimization model. In the studied literature, the main biomass sources are forest (residues) and agricultural residues. The main biobased product applications described are fuel and energy. However, both feedstock and application areas are diversifying. Since 2015 we have seen an increase in type of sources and applications in the publications that we studied.

Over three quarter of the case studies where the location was described, was executed in or based on data from North America; we do not think this is a good representation of the amount of biobased initiatives and research over the world. We also found that studies focus mainly on the strategic decision level and mostly cover the whole supply chain, or at least the supply chain up to the biorefinery. Finally, most of the times models are used to make supply chain design decisions. The fact that biobased logistics models often take a strategic perspective is possibly because biobased supply chains are relatively new, and therefore, a majority of studies will focus on design-related problems.

The abovementioned findings are in line with the results reported in other studies. Acuna et al. (2019) describe that existing biomass logistics and supply chain model studies mainly focus on economic optimization, and Ghaderi et al. (2016) state that most of those models focus on single-feedstock supply chains. Malladi and Sowlati (2018) discuss that most biobased logistics models are about supply chain design of biofuel and bioenergy production, and that recently, supply chain models that focus on other types of biobased products have started to develop.

One of the limitations of the methodology used is that studies that are relevant but do not use the exact wording that we searched for, will not have appeared in the results. Also our scoring method is not completely objective. However, we have aimed to make our literature search as clear and reproducible as possible.

5.2 Biobased Logistics and Sustainability

Many studies mention sustainability as a reason to focus on biobased rather than fossil resources. However, the majority of the models in the studies discussed only have an economic objective. Some studies combine economic and environmental objectives, and even fewer studies also consider a social objective. A recent literature review of biorefinery supply chain design ( Pérez et al., 2017 ) confirm this observation: they show that very few investigations have considered all sustainability dimensions when designing the strategic planning of biorefineries in a territory. The focus on the economic aspects can be explained by the low-profit margin in the biobased sector. Additionally, some of the presented works aim at performing a feasibility study for a specific source or region, and accordingly, economic viability has been a primary concern. Despite this fact, in our opinion, future research should include all three sustainability pillars. Therefore, there is a need for more modelling efforts that look at economic, environmental and social objectives in a holistic way.

In operations research, especially sustainability-focused studies, often a trade-off between multiple objectives is calculated. However, in the studies analyzed for this review, only few authors incorporated such a trade-off curve. Therefore, this is a possible future research direction as well, which was also pointed out by Malladi and Sowlati (2018) . In terms of methodology, this will imply using, for example, Multi-Attribute Decision Making models, Multi-Criteria Decision Making models and the epsilon-constraint method ( Mavrotas, 2009 ; Huang et al., 2011 ).

5.3 Biobased Logistics and Complexity

Almost all studies are about real case studies. Generally, they combine model and a specific biomass source and a biobased product application. However, over the studied decade, there has been an increase in using the more general term “biomass.” So both source and application remain unspecified. The complexity of the supply chain models has also increased. At the beginning of the decade, most studies applied operations research methods to a case study and more recently, the complexity of biobased logistics (with uncertain and seasonal supply, and multiple inputs leading to multiple outputs) has given rise to supply chain models that are not focused on solving a real life problem but on solving an abstract version of a problem. The added complexity is also a reason for the recent increase in the use of heuristics and different types of mathematical approaches. Other studies confirm these observations: Ko et al. (2018) describe that biomass supply chains are more complex than standard fossil-based supply chains due to their specific challenges. Atashbar et al. (2016) mention that there is a need for further integration and optimization of the whole supply chain, taking into account more complexity. This current study shows that biobased logistics models are starting to develop into that area.

This study showed an increase in biomass sources and products. In the definition we used, feed and food are not part of the biobased products. However, several studies considered feed as a biobased outlet, and there was even one study that also considered food as biobased outlet. With the introduction of a further collaboration between food and biobased sectors, supply chains will become more integrated and the distinction between the biobased economy and the bioeconomy will become less clear. Guo et al. (2020) do consider food, feed, and biobased together and show that these chains can be complementary.

6 Future Research Agenda

6.1 a global perspective.

Most of the studies we analyzed base their findings on case studies in North America. Other parts of the world are underrepresented in this topic, while geographical differences influence whole biobased supply chain. Therefore, we encourage scholars to pick a case study outside of North America.

6.2 Biobased Supply Chain Integration

Of the studied papers, 13% consider supply chain collaboration or integration. An integrated approach can help to deal with uncertainty in supply because of, for example, fluctuating yields. In the case of a biorefinery, integrating supply chains can help to benefit from economies of scale. The integration would also imply involving multiple actors (or their interests) in the decisions process. A modelling approach to tackle this is (cooperative) game theory. Only a couple of articles used such a multi-actors methodology for analyzing decisions in a biobased supply chain [i.e., ( Jonkman et al., 2019 ) and Golecha and Gan (2016) ]. However, most other studies assume there is only one actor (or problem owner) or one objective function for all supply chain actors. Considering multi-actor characteristics of biobased supply chains is an important direction for future research. In terms of methods, game theory and agent-based modelling are potentially useful to tackle the challenges in integration and collaboration in biobased chains. Torres et al. (2015) divide use a multi-actor approach by dividing a biorefinery optimization problem in two subproblems: the supply and the demand of intermediates. The actors on both sides of the problem can optimize their part separately while their solutions are coordinated to ensure a feasible biorefinery complex. In their follow-up study, they propose and develop a game theoretical framework and specific methodologies, which allow the optimal design of distributed processing systems, through the decentralized strategies of independent actors ( Torres and Stephanopoulos, 2016 ). Their approach, combining chemical engineering, mathematical optimization and game theory, is very promising. However, only one of the citing studies uses game theory ( Chang et al., 2018 ) but not in a biorefinery context. Thus, there is still enough room for researchers to cooperate and explore this research direction.

6.3 Supply Chain Resilience

Since supply chains are becoming more interconnected and interdependent, supply chain disruptions can become a risk. To deal with this, supply chain resilience has been studied in different contexts, for example, the chemical supply chains ( Behdani et al., 2019 ) and food supply chains ( Bottani et al., 2019 ). One study in our review database incorporates resilience ( Maheshwari et al., 2017 ). They study resiliency optimization of the biomass to biofuel supply chain. To deal with future challenges, more studies on the biobased economy should consider incorporating resilience in their supply chain models.

6.4 Circularity in Biobased Supply Chain Models

In many studies, waste streams are considered as a source of biomass. However, only two studies in our literature database considered circularity or had a circular perspective. Our study has shown that the biobased economy does not only include relatively lower value biofuel and bioenergy products, but also higher value biobased products such as biochemicals and biomaterials that can potentially be re-used. Therefore, the scope of biobased logistics supply chain models should extend to also consider use (so also include the demand side) and re-use and take a circular perspective. This will further increase the complexity of the models because of fluctuation in supply, in expected yield and in composition. Moreover, these re-use streams are often difficult to preserve ( De buck et al., 2020 ). The study of Yeo et al. (2020) is an example of how we expect more future research to consider circularity and complexity in biobased logistics is. They model circular supply chain design for palm oil by, for example, reusing the waste streams for produce electricity. In their supply chain model, they integrate the process design of biorefineries. Also, graph theory is used to test the supply chains.

In this study, we grouped a study into “studies the whole supply chain” it started with the biobased source (e.g., tree trunks) and considered the supply chain up to the market. However, biomass is generally grown on land and the market involves consumers that use a product and dispose of it (depending on the nature of the product). Considering circularity in supply chains is more than considering re-use of water in a biorefinery, it involves a cradle-to-cradle approach where the use and re-use is considered in the design phase ( Muscat et al., 2021 ). It requires a holistic perspective and collaboration with people from other disciplines.

Because of externalities like carbon emissions, we expect policies to stimulate the use of biobased products and to reduce, and in time phase out, the use of fossil products. Both fossil and biobased products fulfil a demand for carbon. When that demand cannot be fulfilled from fossil resources anymore, it needs to be fulfilled by recycling, by using biomass and by converting CO 2 into useful products using Carbon Capture and Utilization (CCU) technologies ( Muscat et al., 2021 ). CCU technologies can be combined with bioconversions, as they often emit or consume carbon dioxide. These technologies are under development and to support these developments, smart supply chain decisions should be made. This is a promising topic for future research.

6.5 The Demand Side

Next to enabling circularity, another reason to consider the demand side is that will help choosing to produce in-demand, higher value products. This will become more relevant when moving from fuel and energy production to different products. To predict demand, studies could use macro-economic models to predict which products will be in demand and adjust the processes in the biorefinery accordingly.

The food supply chain is an example of a chain that is more demand-oriented, and research in food supply chain management has focused on valorization and diversification of the different (side) streams in the production process. Also, there is a vast amount of literature focusing on food quality management in the supply chain, for example ( Tromp et al., 2016 ; Stellingwerf et al., 2021 ). As biomass supplied in biobased logistics can be subject to decay, models considering food quality in logistics could be used as in inspiration to develop models that help optimize the supply and product quality in the biobased supply chain.

Biobased supply chains (and models describing them) are increasingly becoming multi-feedstock, and multi-application, which means that there are an increasing number of variables to consider when making an optimal decision. Next to this increased complexity, multiple objectives should be considered to be able to meet sustainability requirements, and those objectives can be conflicting. This will make decisions and models to support those decisions increasingly complex. To deal with that complexity, future research should continue to incorporate methods that help reduce calculation time.

6.6 Sustainability and Collaboration in Biobased Logistics

To make logistics and supply chain models contribute to a sustainable biobased economy, researchers should aim to incorporate people, planet and profit objectives and circular perspectives into their studies. This will cause increased complexity, which requires new methodologies and collaboration from researchers from different fields and backgrounds. Our study shows the complexity of biobased logistics: knowledge from different fields such as chemical processes in bioreactors, sustainability analysis, operations research and mathematical modelling, needs to be combined to address challenges in these fields. This way, these biobased logistics supply chain models can contribute to tackling the challenges that the biobased economy has to offer.

Author Contributions

HS: concept development, data gathering, data analysis, writing article, revising article. EA: concept development, writing article, reviewing. BB: concept development, data analysis, writing article, reviewing. XG: concept development, reviewing.

This work is part of the research programme TransSonic, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO); also it was funded by Wageningen Food and Biobased Research.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

Special thanks to the experts who joined the seminar on this study for their valuable feedback and opinions on the topic and their future research suggestions.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fceng.2022.778315/full#supplementary-material

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Keywords: bioeconomy, biorefinery, optimization, simulation, sustainability

Citation: Stellingwerf HM, Guo X, Annevelink E and Behdani B (2022) Logistics and Supply Chain Modelling for the Biobased Economy: A Systematic Literature Review and Research Agenda. Front. Chem. Eng. 4:778315. doi: 10.3389/fceng.2022.778315

Received: 16 September 2021; Accepted: 13 April 2022; Published: 09 May 2022.

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Copyright © 2022 Stellingwerf, Guo, Annevelink and Behdani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Helena Margaretha Stellingwerf, [email protected]

This article is part of the Research Topic

Optimization Methods for Biorefineries and Bio-based Supply Chains

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Circular economy, bioeconomy, and sustainable development goals: a systematic literature review

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The circular economy (CE) and bioeconomy (BE) are recognized as potential solutions for achieving sustainable development, yet little research has examined their potential contribution to the United Nations’ Sustainable Development Goals (SDGs). In this study, we conducted a bibliometric analysis of 649 articles published between 2007 and 2022, as well as a systematic literature review of 81 articles, to assess the extent to which the CE and BE communities have addressed the SDGs. Our analysis identified 10 research gaps including the limited number of empirical quantitative papers, particularly in the context of BE, and the underrepresentation of developing regions such as Latin America and Africa in the literature. Our main finding reveals that the CE community primarily focuses on SDG 12, Responsible Consumption and Production, followed by SDG 9, Industry, Innovation, and Infrastructure; SDG 7, Affordable and Clean Energy; and SDG 6, Clean Water and Sanitation. The BE community, on the other hand, focuses primarily on SDG 7, followed by SDG 9 and SDG 12. However, both communities lack attention to social SDGs such as quality education, poverty, and gender equality. We propose that a combination of CE and BE, known as circular bioeconomy, could help countries achieve all SDGs. Further research is needed to develop and implement circular bioeconomy policies that address these gaps and promote sustainable development. In this sense, our study identified an important research gap that needs more attention in the future.

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Introduction and theoretical framework

The United Nations (UN) 2030 Agenda for Sustainable Development (Cf 2015 ) resolution has been challenging both developed and developing economies to achieve sustainable development (Assembly 2015 ; Khan et al. 2019 ). This has led to the establishment of 17 Sustainable Development Goals (SDGs) with 169 targets and 213 measurable indicators aimed at promoting well-being in economic, social, and environmental aspects (Costanza et al. 2016 ). However, these SDGs can differ among regions, countries, and local policymakers, requiring national plans for achieving sustainability (Belmonte-Ureña et al. 2021 ). To this end, various political agendas have been initiated globally under diverse concepts. For instance, the European Commission introduced the bioeconomy (BE) concept in 2012 (EC 2012 ) and the circular economy (CE) notion in 2015 (E. EC 2015 ). Similarly, China applied CE policy tools from the early 2000s (D'Amato et al. 2017 ; Murray et al. 2017 ), while the United States of America adopted the bioeconomy through a national blueprint (D'Amato et al. 2017 ).

The decline of sustainability in the wake of the global COVID-19 pandemic has sparked a significant demand for fresh research on environmental issues. Scholars have put forth various proposals to address this concern, introducing new micromodels such as The Sustainability Pyramid (Lim 2022 ). This framework advocates for the adoption of a hierarchical approach to promote sustainability, thereby facilitating the achievement of the Sustainable Development Goal 12. Additionally, researchers have also developed macromodels centered around the concept of the sharing economy, which fosters a novel economic paradigm in the digital age (Tham, Lim, & Vieceli 2022 ). It is worth noting that the successful implementation of changes in the field of bioeconomy necessitates the utilization of novel technologies, processes, and practices, all of which require collective action on the part of consumers. Hence, the bioeconomy approach underscores the importance of exploring consumers’ perspectives and embracing shared responsibility in contributing to the development of bio-based products and services (Wilke et al. 2021 ). Moreover, the COVID-19 crisis has highlighted the urgent need for innovative responses across various domains. These responses encompass a wide spectrum of innovations, ranging from technological advancements to frugal and social innovations (Dahlke et al. 2021 ). Against this backdrop, circular economy emerges as a promising alternative to the aforementioned macromodels, as it has been thoroughly examined and evaluated in the present study.

The concepts of bioeconomy and circular economy have been developed over time and are considered complementary (del Mar Alonso-Almeida and Rodriguez-Anton 2019 ; McCormick and Kautto 2013 ; Rodriguez-Anton et al. 2019 ). The bioeconomy is concerned with the conversion of renewable biological resources into various materials, chemicals, and energy, such as food, feed, bio-based products, and bioenergy (EC 2012 ; O’Brien et al. 2017 ). It is a sustainable strategy based on life science innovations (Maciejczak & Hofreiter 2013 ), which generate competitiveness, economic development, and low-carbon growth (EC 2012 ; O’Brien et al. 2017 ). Conversely, the circular economy concept emerged from the literature on industrial symbiosis (D'Amato et al. 2017 ; Mishenin et al. 2018 ), which aims to reduce the environmental impacts of economic actors by reducing, reusing, recycling, and recovering materials during the productive process and consumption (Kirchherr et al. 2017 ; MacArthur 2013 ; Murray et al. 2017 ). Circular economy tackles various modern societal issues (Khan, Sharif, & Mardani), such as increasing demand for resources, population growth, consumption, and price volatility of raw materials, promoting better environmental performance and socioeconomic prosperity (Kirchherr et al. 2017 ). While these concepts are still being developed in parallel, some authors believe that bioeconomy and circular economy reinforce each other (D'Amato et al. 2017 ; Hetemäki et al. 2017 ). Recently, the concept of circular bioeconomy has emerged, which describes the circular and efficient use of renewable non-fossil raw materials and products (D'Amato et al. 2017 ; Sharif et al. 2019 ), providing a better understanding of sustainable development.

Bioeconomy and circular economy are closely related to several Sustainable Development Goals (EC 2012 ; E. EC 2015 ; Rodriguez-Anton et al. 2019 ). Bioeconomy might help the social aspects by creating jobs in agriculture and industry (SDG 8), the economic aspects by boosting innovation (SDG 9) and economic growth (SDG 8), and improving the environmental performance by reducing the use of resources through efficient use of natural resources (SDG 12) and increasing bioenergy (SDG 7) (O’Brien et al. 2017 ). Circular economy also helps achieve responsible consumption and production (SDG 12) through resource efficiency mechanisms and green growth (Rodriguez-Anton et al. 2019 ). According to the European Commission (E. EC 2015 ), CE is related to several sectors (i.e., infrastructure, health, education, industry, and agriculture) to promote new investments, employment, and economic growth. These linkages help several SDGs, such as decent work and economic growth (SDG 8); industry, innovation, and infrastructure (SDG 9); sustainable cities and communities (SDG 11); responsible consumption and production (SDG 12); and climate action (SDG 13) (Rodriguez-Anton et al. 2019 ). However, there is no consensus about the implications of the bioeconomy and circular economy for other Sustainable Development Goals. For example, a growing bioeconomy might increase the scale of global land use, affecting access and food price (Heimann 2019 ). Moreover, several studies analyze the potential negative impacts of the circular economy on sustainable development, especially those related to social inclusion and climate change (Belmonte-Ureña et al. 2021 ; D'Amato et al. 2017 ; Sehnem et al. 2019a , b ).

Several studies have explored the potential of a circular bioeconomy (CBE) in contributing to the achievement of Sustainable Development Goals (SDGs). For instance, some authors have focused on analyzing the sources and production of agricultural waste and proposed pathways for further value addition through the application of various technologies, including biorefinery solutions. This bioeconomy perspective not only helps in reducing agricultural waste but also enables the generation of a wide array of value-added products within the economy (Kumar Sarangi et al. 2023 ). Additionally, other researchers have emphasized the role of CBE models in reducing reliance on fossil fuels. Specifically, they have highlighted the significance of biomethane as a flexible and environmentally friendly resource for mitigating climate change. The profitability associated with biomethane production has been found to have the potential to influence the energy policy landscape, thereby shaping future scenarios (D'Adamo et al. 2023 ). These studies collectively underscore the transformative potential of circular bioeconomy in contributing to sustainable development, addressing agricultural waste, and providing alternative resources for clean energy production.

The objective of this study is to investigate the literature on bioeconomy and circular economy in the context of SDGs. Specifically, this study aims to explore the relationship between bioeconomy and circular economy and their impact on economic, social, and environmental aspects. Despite the increasing interest in bioeconomy and circular economy, there is still a significant gap in the understanding of how these concepts interact with each other and their influence on SDGs. Moreover, there is a lack of knowledge about which SDGs are predominantly influenced by each concept and how the combination of bioeconomy and circular economy can contribute to sustainable development. Therefore, this study aims to address the following research questions: (1) What is the relationship between bioeconomy and circular economy in the context of SDGs? (2) Which SDGs are mainly influenced by bioeconomy and circular economy, respectively? (3) How can the combination of bioeconomy and circular economy contribute to sustainable development? (4) Which types of databases, unit analysis, and geographical areas are under investigation in selected literature? (5) What are the main literature gaps, and what is the future research agenda of the bioeconomy and circular economy linked to the SDGs? By answering these research questions, this study aims to provide a better understanding of the relationship between bioeconomy and circular economy and their potential contribution to sustainable development.

Previous research has examined the bioeconomy and circular economy concepts using bibliometric or systematic literature review methods (D'Amato et al. 2017 ; Ferreira Gregorio et al. 2018 ). In addition, some scholars have combined bioeconomy or circular economy with other aspects, including circular business models (Wiebke Reim et al. 2019 ; Suchek et al. 2021 ), eco-innovation (Prieto-Sandoval et al. 2018 ), business organizations (Salvador et al. 2022 ; Sarja et al. 2021 ), development strategy (Papadopoulou et al. 2022 ), and regional analysis (Arsova et al. 2022 ). However, only a few studies have analyzed both bioeconomy and circular economy concepts (D'Amato et al. 2017 ; Ferreira Gregorio et al. 2018 ; Salvador et al. 2022 ), and just one has linked bioeconomy to Sustainable Development Goals (Biber‐Freudenberger, Ergeneman, Förster, Dietz, & Börner 2020 ). The previous literature on the bioeconomy and circular economy is summarized in Table 1 , which was developed through bibliometric or systematic review methods.

To the best of our knowledge, an integrated structured literature review that relates the bioeconomy, circular economy, and their connection with Sustainable Development Goals (SDGs) is still missing. While some studies have explored the topics separately (as summarized in Table 1 ), they did not consider the intersections between the concepts, leading to a research gap. Moreover, none of the reviewed studies used quantitative analysis to avoid an eclectic approach that may neglect important factors or overemphasize others (Ferraz et al. 2021 ). By expanding the scope from circular economy to related keywords, such as bioeconomy, we can identify key topics and analyze how the fields can learn from each other. We focus on bioeconomy and circular economy because they both propose strategies for sustainable development worldwide (D'Amato et al. 2017 ), and their fragmentation hinders their progress (O’Brien et al. 2017 ). Thus, it is essential to evaluate the extent to which research conducted with these concepts contributes to addressing the SDGs’ challenges.

In this sense, this article identifies the relevance of published articles in BE and CE with the targets to impact sustainable development. Note that our article corroborates with a collection of reviews on the Sustainable Development Goals, extending current reviews on business innovation to economic models (Azmat, Lim, Moyeen, Voola, & Gupta 2023 ). Thus, we systematically analyzed the relevant international literature on bioeconomy and circular economy linked to Sustainable Development Goals (SDGs). Our research identified that a systematic literature review based on this broader approach identifies structural gaps and future research agenda regarding bioeconomy and circular economy planning to achieve Sustainable Development Goals. The bibliometric tool helped us to identify the primary types of methods and databases used and the leading journals and authors for each research area. In addition, the combination of bibliometrics with an in-depth qualitative analysis of the research contents of the most relevant articles helped to identify research gaps in terms of data, topics, and methods, as well as reveal opportunities for mutual learning between different overlapping, or separate, areas of the literature.

Consistent with the systematic literature reviews conducted by other authors (Lim, Kumar, & Ali 2022a , b ; Mukherjee et al. 2022 ), our study contributes to the advancement of theory and practice in the field. Firstly, employing the literature review method enabled us to systematically investigate the findings of the articles under analysis and establish connections with the most prominent research topics. As a result, we identified two distinct clusters within the domains of circular economy (CE) and bioeconomy (BE) that displayed limited interconnectivity. This finding highlights the need for closer integration and collaboration between these clusters. Secondly, the systematic literature review facilitated a comprehensive understanding of the essential research in greater detail. Consequently, we identified several limitations within both research communities, including the scarcity of quantitative studies, restricted coverage of databases, and limited geographical regions represented in the literature. Thirdly, our study contributes by identifying ten research gaps in the existing literature and presenting associated challenges that can guide future research endeavors. By shedding light on these gaps, we aim to encourage further exploration and investigation in these areas. Finally, to provide a comprehensive overview, we presented a matrix illustrating the intricate relationship between bioeconomy, circular economy, circular bioeconomy, and each of the 17 Sustainable Development Goals (SDGs). Through this analysis, we discovered that research efforts should prioritize the exploration of social SDGs, emphasizing the need for studies that address the social dimensions of sustainable development. By offering these insights and findings, our study enhances the current understanding of the field and provides a foundation for future research to address the identified research gaps and contribute to the achievement of the SDGs.

The structure of this article comprises several sections that aim to present a comprehensive analysis of the bioeconomy and circular economy concepts related to Sustainable Development Goals. “ Materials and methods ” outlines the data and methods applied in this study. In this section, the authors describe the data sources and the procedural methods used to conduct the bibliometric and systematic literature review. “ Results ” presents the main findings of the study, including bibliometric results and the citation network’s structure. This section also highlights the main literature clusters and provides insights into the research strategies, geographic areas, and scopes of the analyzed articles. The Results section also provides a systematic analysis of the central insights of the bioeconomy and circular economy communities, identifying ten research gaps that require further investigation. “ Final remarks ” summarizes the main findings of the study and discusses their implications for future research.

Materials and methods

This section presents the materials and methods that allowed us to answer the research question. This analysis combines Bibliometrics and Systematic Literature Review techniques. Several studies have used the Bibliometric tool to provide quantitative information about prominent authors, keywords, and citation networks (D'Amato et al. 2017 ; Suchek et al. 2021 ). This technique presents several advantages, such as analyzing a significant volume of scientific studies (Belmonte-Ureña et al. 2021 ; Donthu et al. 2021 ; Mukherjee et al. 2022 ; Snyder 2019 ). As posited by Donthu et al. ( 2021 ), bibliometric analysis can be classified into two primary categories. Firstly, performance analysis serves to evaluate the contributions of research constituents, shedding light on their individual achievements and impact. Secondly, science mapping delves into the interrelationships between these constituents, providing insights into the connections and networks that exist within the scientific landscape.

Furthermore, our approach to bibliometric analysis aligns with the framework proposed by Lim and Kumar ( 2023 ). By employing the 3S sensemaking principle—namely, scanning, sensing, and substantiating—we have effectively navigated the bibliometric landscape (Lim & Kumar). The initial scanning phase facilitated the systematic collection and organization of pertinent data, forming a robust foundation for subsequent phases. Moving beyond superficial observations, the sensing stage delved into intricate patterns, unveiling the underlying themes, trends, and fundamental drivers that characterize the field. The ultimate step, substantiating, establishes the credibility and reliability of our findings, thereby ensuring their resilience under rigorous examination. Through the lens of bibliometrics, we have critically assessed the scholarly output, encompassing publications, and gauged the scientific influence through citations, both at the level of research works (such as articles) and their contributors (comprising authors and geographic origins). Furthermore, our analysis has unveiled pivotal subjects including bioeconomy and circular economy, as well as their intersection in the circular bioeconomy. By scrutinizing various dimensions—social, economic, and environmental—we have shed light on key thematic areas. This multifaceted exploration has not only highlighted notable trends but has also illuminated existing gaps that warrant further investigation.

According to Lim and Kumar ( 2023 ), the bibliometric tool is an analytical technique usually combined with systematic literature reviews. This occurs because bibliometric studies failed to delve deeper into research communities, relevant concepts, and knowledge (Belmonte-Ureña et al. 2021 ). For this reason, we used the Systematic Literature Review technique, which presents advantages to deeply understanding research areas, especially in revealing research gaps (Alves & Mariano 2018 ; Ferraz et al. 2021 ; Jabbour 2013 ; Lim, Kumar, et al. 2022a , b ; Mariano, Sobreiro, and do Nascimento Rebelatto 2015 ; Paul et al. 2021 ; W. Reim et al. 2015 ; Snyder 2019 ; Tranfield et al. 2003 ). In this sense, this article combined these techniques with some complementary steps, which can be summarized as follows:

Step 1: The keywords were refined through the analysis of keywords co-occurrence network and keywords used in previous studies (these studies are presented in Table 1 ).

Step 2: Through the pre-established keywords, we assessed the articles published in major databases.

Step 3: Screen and select articles reading their titles and abstracts.

Step 4: Create a protocol to classify publications, and we applied this protocol to the publications screened.

Step 5: Selection of articles for an in-depth analysis.

Step 6: Create the scientific production profile of each publication analyzed, revealing the main research strategies.

Step 7: Scope analysis of each filtered article based on geographical area, unit of analysis, and area/sector under investigation.

Step 8: Systematization of the results obtained in the four analyses conducted (bibliometric, citation network, research strategies, and scope) to reveal research gaps and future research agenda.

Figure  1 shows the PRISMA protocol with the steps of systematic literature review selection.

PRISMA protocol

The first step was defining the keywords. Some studies pointed out that keywords must be chosen by reading previous literature (Kraus et al. 2022 ). In this sense, we analyzed 12 previous studies using systematic literature about bioeconomy and circular economy. Then, we combined these keywords with Sustainable Development Goals (SDGs). We refined these keywords through preliminary searches of the Web of Science (WoS) database. These preliminary keywords were essential for creating the co-occurrence network using the VOSviewer software. The co-occurrence network was crucial to identify relevant synonyms.

Using the keywords selected in Step 2, we searched articles on the WoS database in December 2022, based on the title, abstract, and keywords for articles, without any time or language restriction. When conducting research, authors are often faced with the decision of selecting the appropriate databases to utilize. Among the options available, Scopus and Web of Science (WoS) are widely recognized as the largest scientific databases housing a plethora of academic articles (Kraus et al. 2022 ; Snyder 2019 ). To mitigate the risk of obtaining biased findings resulting from the limitations of a single database, researchers may opt to employ multiple databases (Kraus et al. 2022 ). In the present study, however, we chose to focus exclusively on the WoS database. This decision was based on several factors. Firstly, WoS boasts the most comprehensive global collection of articles and publishers pertaining to bioeconomy, circular economy, and Sustainable Development Goals (SDGs). By utilizing this database, we were able to access a broad range of relevant scholarly material. Furthermore, the decision to employ the WoS database aligns with the established practices of numerous systematic literature reviews, lending further credibility to our study (Alves & Mariano 2018 ; Ferraz et al. 2021 ; Jabbour 2013 ; Tranfield et al. 2003 ).

Figure  2 illustrates the keywords used in Step 3. Each of the keywords related to bioeconomy (blue) and circular economy (green) was combined with each of the keywords related to Sustainable Development Goals (gray). The keyword combinations show articles that analyze bioeconomy and circular economy combined with SDGs.

Keywords used in this research “*” Replaces one or more characters of a word, for example “Econom*” also includes the expressions “Economics” and “Economies”

In accordance with Kraus et al. ( 2022 ), the systematic literature review method encompasses a screening process. This process begins with the identification and elimination of duplicate results obtained from databases. Subsequently, abstract screening is employed to exclude studies that do not align with the research criteria. Finally, the remaining documents undergo full-text screening to ensure their relevance and suitability for inclusion in the review. In this sense, we conducted Step 3. We verified the adherence of 741 publications based on reading the title and abstract. Some articles were excluded because they did not consider the entire research topic (17 articles), they were editorials (12 articles), and they were written in the non-English language (11 articles). We also found that 52 articles were duplicated in bioeconomy and circular economy research areas. Then, the final database presents 649 publications.

In Step 4, the publications were classified according to bioeconomy and circular economy research areas, as well as at least one Sustainable Development Goal (SDGs 1–17). This classification was necessary to create the citation network and bibliometric analysis. In Step 5, we selected the publication for the in-depth analysis. During this step, a complete reading of the 81 articles was made. Our number of articles for in-depth analysis is higher than the average (78 articles) of previous systematic literature reviews on the bioeconomy and circular economy (Arsova et al. 2022 ; Gil Lamata and Latorre Martínez 2022 ; Papadopoulou et al. 2022 ; Wiebke Reim et al. 2019 ; Sarja et al. 2021 ; Suchek et al. 2021 ). The in-depth analysis allowed us to identify research strategies (Step 6) and to perform the scope analysis (Step 7).

Finally, in Step 6, we conducted a structured review to present bibliometric, citation network, main research strategies, and scope analysis of bioeconomy and circular economy separately. Other authors pointed out that this approach is adequate to reveal research gaps and opportunities for future research (Step 8) (Alves & Mariano 2018 ; Ferraz et al. 2021 ; Mariano et al. 2015 ). In this sense, the bibliometric technique revealed the publication growth, the most relevant publications and authors, and the most relevant journals for bioeconomy and circular economy. The network analysis was crucial to illustrate this research area’s main references and clusters. The primary research strategies analysis revealed the main methods used to investigate the problems of the bioeconomy and circular economy considering the SDGs.

Moreover, the exploration of sustainability approaches has been enriched by the contributions of various authors utilizing the multi-study technique (Lim 2023 ; Lim, Ciasullo, Douglas, & Kumar 2022 ). Notably, Lim et al. ( 2022a , b ) employed a meta-systematic review approach, encompassing multiple studies, to examine the synergistic relationship between Environmental Social Governance (ESG) and Total Quality Management (TQM). Similarly, Lim ( 2023 ) adopted a methodological approach incorporating multiple studies to assess the progress of consumption research and propose strategies aimed at inspiring consumers to embrace environmentally friendly practices while also promoting human well-being. Despite the significance of the multi-study strategy in advancing sustainability research, our study offers a comprehensive analysis that encompasses the geographical scope, unit of analysis for each publication, and the most extensively investigated areas and sectors within the realms of bioeconomy (BE) and circular economy (CE) studies. By providing this valuable insight, our research expands the existing knowledge base and enhances understanding in these fields.

This section presents the results of the bibliometric and systematic literature review analysis conducted in this study. A bibliographic database search of the Web of Science identified 649 publications relevant to bioeconomy and circular economy. Using bibliometric techniques, we analyzed this dataset and narrowed down the selection to 81 relevant publications for the systematic literature review. The review process involved a detailed analysis of 17 publications on bioeconomy and 67 publications on circular economy. This selection process was necessary to ensure that the number of publications under analysis represented the most relevant and up-to-date articles in both research areas.

Bibliometric analysis

Examining publication growth over time is one of the most critical aspects of bibliometric analyses. In our study, we observed a consistent increase in the number of publications related to the bioeconomy and circular economy in association with Sustainable Development Goals from 2007 to 2022. Figure  3 depicts the number of articles published in each research community, indicating a steady upward trend in both areas over time.

Publication growth per research area (bioeconomy and circular economy)

In general (BE and CE), from 2007 to 2016, the number of publications was unimpressive. Interestingly, even after the Europe Commission reports (EC 2012 ; E. EC 2015 ), some years were necessary for studies on bioeconomy and circular economy linked to SDGs to appear. The curve presented exponential publication growth since 2017, which reveals an annual average rate of 202% (2017–2022). The bioeconomy curve did not present publications from 2007 to 2015. However, from 2017 to 2022, we found 135 articles linking the bioeconomy to SDGs, representing an average publication growth of 109% per annum (p.a.). Moreover, the circular economy curve presents exponential growth since 2017, showing an average publication growth of 192% p.a. In this sense, the circular economy curve shows an increasing slope while the growth of publications in the bioeconomy shows relatively less substantial growth during the last years.

Academic journals are critical in disseminating knowledge, particularly to research communities and scientific audiences. Figure  4 highlights the number of publications per research area in the top 10 most relevant journals. For the bioeconomy community, Sustainability (Switzerland), New Biotechnology, Renewable and Sustainable Energy Reviews, Journal of Cleaner Production, Science of the Total Environment, Forest Policy and Economics, Journal of Environmental Management, Environment Development and Sustainability, Amfiteatru Economic make up 50% of the publications found. For the circular economy community, the top 10 relevant journals are Sustainability (Switzerland), Journal of Cleaner Production, Journal of Industrial Ecology, Resources Conservation and Recycling, Science of the Total Environment, Energies, Sustainable Production and Consumption, Journal of Environmental Management, Applied Sciences-Basel, and Business Strategy and the Environment, which make up 41.34% of the publications found. Remarkably, the analysis of scientific publications in various journals reveals only one published paper for bioeconomy (59) and 209 papers for circular economy. These findings suggest that these concepts are still fragmented in the literature and associated with diverse research approaches.

Most relevant journals: a bioeconomy; b circular economy

The application of citation analysis serves as a valuable tool for determining the significance of scientific publications. This study employed local citations to analyze the most frequently cited papers within the analyzed network. Table 2 showcases the most notable publications regarding local citations in the focus network. Utilizing local citations facilitated the identification of studies that occupy leading positions within the various analyzed clusters. It is important to note that recent papers may not have had sufficient time to accrue prominence (Mariano et al. 2015 ). The most locally cited article in the bioeconomy network was by Dietz et al. (Dietz et al. 2018 ). This study proposes strategies for promoting the expansion of the bioeconomy to achieve Sustainable Development Goals (SDGs) through effective governance tools. The most frequently cited article within the circular economy network was Schroeder’s (Schroeder et al. 2019 ), which examines the potential of the circular economy to address SDGs. Other notable publications include studies that analyze circular economy strategies and assess the impact of COVID-19 on the global economy and ecosystems (Ibn-Mohammed et al. 2021 ), as well as investigations into the challenges and opportunities presented by biorefineries in the European Union to bolster bioeconomy (Hassan et al. 2019 ), among others.

Main research strategies and geographical analysis

This subsection analyzes the research strategies employed in the 81 publications selected for the in-depth analysis and the primary databases and methods used over the years. The reviewed articles are categorized into five research-method categories: (i) literature review; (ii) empirical-quantitative studies, which use quantitative techniques to draw general conclusions about a specific issue using a sample of observations; (iii) empirical-qualitative studies, which employ descriptive data analysis and/or discuss case studies; (iv) theoretical studies with quantitative analyses that develop a new theory and test it; and (v) theoretical-conceptual studies, which present new theoretical frameworks and conceptual models. These categories allow us to assess the dominant research methods used in the field and identify gaps and opportunities for future research (Fig.  5 ).

Classification by research method

For both research communities (represented by gray bars), we found a predominance of literature review studies (70.37%), followed by empirical-quantitative (14.81%), empirical-qualitative (12.35%), theoretical studies with quantitative analyses (1.23%), and theoretical-conceptual studies (1.23%). The blue bars represent the bioeconomy community, which shows a high concentration of publications using the literature review method (82.35%), followed by empirical-qualitative (11.76%) and empirical-quantitative (5.88%) studies. No studies were found using theoretical studies with quantitative analyses and theoretical-conceptual studies methods. The green bars represent the circular economy community, which presents most of its publications using the literature review method (67.19%), followed by empirical-quantitative (17.19%), empirical-qualitative (12.50%), theoretical studies with quantitative analyses (1.56%), and theoretical-conceptual studies (1.56%). In summary, Fig. 7 reveals a high concentration of studies using literature review as a research method, particularly in bioeconomy studies, and more empirical studies are needed for both research communities.

The present analysis focuses on studies that utilized a unit of analysis (as depicted in Fig.  6 ), which is crucial considering the limited employment of quantitative methods in the bioeconomy and circular economy fields.

Classification by analyzed units

Results indicate that the majority of studies investigating bioeconomy and circular economy are concerned with countries (50%), followed by companies or enterprises (20%), other regions (such as metropolitan areas, islands, etc.) (20%), municipalities or cities (6.67%), and finally, colleges and universities (3.33%). However, there is a dearth of research on regional development, particularly in the context of bioeconomy, as no studies analyzing regions were identified in this research community.

The limited number of regional studies in bioeconomy and circular economy research may be attributed to the dearth of available databases. Among the 81 publications analyzed, only two studies employed international databases, such as the World Bank (Coscieme et al. 2020 ) and the Eurostat (Rodriguez-Anton et al. 2019 ). Other studies relied on literature review tools such as Web of Science, Scopus, and Google Scholar, as well as other primary databases, including Confederation of Navarre Entrepreneurs (Pla-Julián & Guevara 2019 ), Curaçao Environmental Statistics, and Curacao Tourism Board (Fuldauer et al. 2019 ). The lack of databases is consistent with other research gaps, such as the absence of longitudinal analyses that can provide insight into the evolution of the bioeconomy and circular economy over time. Furthermore, there is a limited number of studies that create indicators to offer policy recommendations on bioeconomy and circular economy, with only a few examples including the circular economy Index developed by Rodriguez-Anton et al. (Rodriguez-Anton et al. 2019 ), the Detrended Rate Matrix used by Ravanelli et al. (Ravanelli et al. 2018 ), and the indices for the lead recycling enterprise investigated by Pan et al. (Pan et al. 2019 ).

The present study further examined the geographical scope of the analyzed publications, as shown in Fig.  7 . Out of the 81 studies analyzed, 31 were found to have a specific geographical focus, while 50 were classified as “not applicable” or of a general nature.

Classification by geographical area

Most studies with a specific focus analyzed Europe (48.39%) or Asia (25.81%). In comparison, a smaller number of studies presented a global analysis (6.45%) (Landrigan et al. 2020 ), focused on South America (6.45%) (Pohlmann et al. 2020 ), or investigated countries with a strategy for bioeconomy (3.23%) (Dietz et al. 2018 ), developing countries (3.23%) (Schroeder et al. 2019 ), North America (3.23%) (Ravanelli et al. 2018 ), and OECD countries (3.23%) (Redlingshöfer et al. 2020 ). It is worth noting that bioeconomy publications were primarily focused on Europe (77.78%), which may be attributed to the European Commission reports and the availability of the Eurostat database for bioeconomy. In contrast, circular economy publications showed a more even distribution between Europe (36.36%) and Asia (31.82%). Nevertheless, Fig.  8 highlights a gap in the literature regarding the analysis of developing nations (i.e., Latin America and Africa), which is crucial for guiding sustainable development in these countries.

Science mapping

This subsection presents world maps that depict the number of publications and citations for each research community (Fig.  8 ). The use of dark colors in the maps indicates a higher number of citations or publications than lighter colors. The distribution of publications for bioeconomy across various regions is illustrated in Fig.  8 a, which shows that Central Europe is more concentrated in terms of the number of publications when compared to other developed regions such as the USA and developing regions such as Latin America and Africa. Notably, even countries with bioeconomy development plans, such as Brazil, have low publication counts in this research area. Figure  8 b depicts a similar pattern, with a high concentration of citations observed in Central Europe, particularly in Germany. Figure  8 c portrays the number of publications dedicated to analyzing circular economy, indicating that this research area is more widely dispersed worldwide, with a high number of publications in Europe, the USA, India, China, and Australia compared to developing nations. Figure  8 d provides information on the number of citations for circular economy, which is concentrated in EU member nations and Australia.

Maps of number of publications and citations on bioeconomy and circular economy

Network analysis

Figure  9 presents the citation network generated from the 649 papers selected for our analysis. The individual publications represent the nodes in the network, and the links between them are depicted by arrows that indicate the direction of knowledge flow, with the cited node pointing towards the citing node. The size of each node corresponds to its local citations in the network, which is determined by the absolute number of links that the publication has within the identified main papers in the network. Each research community is represented by a unique color, such as red representing bioeconomy and green representing circular economy.

Citation network

The citation network presented in Fig.  9 provides insights into the interconnections between the identified clusters. The network is composed of two main clusters that are distributed among the bioeconomy and circular economy communities. Small clusters and articles without any connections in the network are not shown to simplify the visualization. The red cluster is associated with the bioeconomy and consists of publications that discuss the relevance of bioeconomy strategies in the context of Sustainable Development Goals (SDGs). For instance, Dietz et al. (Dietz et al. 2018 ) present a theoretical framework for bioeconomy and SDGs, while Hassan et al. (Hassan et al. 2019 ) analyze the transformation of biomass into bioenergy and bioproducts in the EU, highlighting the synergy of bioeconomy with climate change mitigation. Additionally, Mak et al. (Mak et al. 2020 ) discuss the role of the bioeconomy in reducing food waste.

On the other hand, the green cluster focuses on analyzing the circular economy and its relationship with SDGs. This cluster is led by Schroeder’s paper (Schroeder et al. 2019 ), which highlights the importance of the circular economy in achieving sustainable development. Bhatt et al. (Bhatt, Ghuman, and Dhir 2020 ) investigate the intellectual structure of sustainable manufacturing and its link with circular economy practices, while Dantas et al. ( 2021 ) compare the synergies between circular economy and Industry 4.0. Schandl et al. ( 2018 ) use the concept of Industrial Ecology to analyze global material flows and resource productivity. Although not well connected with the green cluster, D’Amato et al. (D'Amato et al. 2020 ) contribute to the idea of a circular bioeconomy.

In summary, the citation network depicted in Fig.  6 highlights a significant disconnect between the bioeconomy and circular economy communities, particularly regarding their contributions to Sustainable Development Goals (SDGs) research. Given the potential for mutual learning between these communities, we thoroughly analyzed 81 articles based on the most cited articles in the literature.

Scope analysis and discussion of main arguments

This subsection presents a scope analysis of the link between bioeconomy and circular economy with Sustainable Development Goals (SDGs). Our main arguments are based on the articles analyzed in this study. Our findings indicate that the link between bioeconomy and circular economy with SDGs is complex and multifaceted to explain Sustainable Development Goals. The articles under analysis highlight the need for a systemic and holistic approach to achieving sustainable development. The bioeconomy and circular economy are viewed as key drivers of sustainability, as they can contribute to reducing greenhouse gas emissions, efficiently using resources, and creating new job opportunities. The distribution of articles according to the Sustainable Development Goals is presented in Table 3 . Each article was classified under at least one main SDG.

Our analysis suggests that the link between bioeconomy and circular economy with SDGs is not yet fully explored. While some articles explicitly address this relationship, others do not. Additionally, we identified a lack of studies analyzing social aspects of sustainable development, such as reducing inequalities and poverty, which are fundamental to achieving SDGs.

Figure  10 depicts the interconnectedness between bioeconomy (blue), circular economy (green), circular bioeconomy (purple), and Sustainable Development Goals. The diagram highlights the limited attention that the bioeconomy and circular economy have given to specific areas of the 17 Sustainable Development Goals, particularly the social aspects. It is worth noting that a combination of bioeconomy and circular economy, referred to as circular bioeconomy by D’Amato (D'Amato et al. 2017 , 2020 ), can expand the synergies between these two concepts and the Sustainable Development Goals, thereby providing a more comprehensive approach.

Relationship between bioeconomy (blue), circular economy (green), circular bioeconomy (purple), and Sustainable Development Goals

Our analysis reveals that most articles concentrate on SDG 12, Responsible Consumption and Production, representing 27.73% of the articles reviewed. Additionally, some articles investigate the synergies between bioeconomy and circular economy with all SDGs, representing 14.64% of the total articles. Other frequently researched SDGs include SDG 9, Industry, Innovation and Infrastructure (13.41%), and SDG 7, Affordable and Clean Energy (12.48%). On the other hand, some SDGs are not well-represented in the bioeconomy and circular economy literature. For instance, SDG 5, Gender Equality, accounts for only 0.77% of the articles reviewed. Similarly, SDG 15, Life on Land, and SDG 17, Partnerships to achieve the Goal, each represent only 0.62% of the articles reviewed. SDG 13, Climate Action, and SDG 16, Peace and Justice Strong Institutions, are also underrepresented in the literature, each accounting for only 0.46% and 0.31% of the articles reviewed, respectively. Finally, SDG 10, Reduced Inequalities, is not represented in any of the articles analyzed.

The bioeconomy community’s research focuses on several Sustainable Development Goals (SDGs) as demonstrated by the findings in Table 3 . The primary focus is on SDG 7. Affordable and Clean Energy, accounting for 23.53% of the publications, followed by SDG 9 Industry, Innovation and Infrastructure (21.32%), synergies between bioeconomy with all SDGs (19.12%), and SDG 12. Responsible Consumption and Production (8.09%). For instance, Heimann’s research (Heimann 2019 ) suggests that bioeconomy may help achieve all SDGs, although there may be trade-offs between the SDG targets. Similarly, Ronzon and Sanjuan (Ronzon and Sanjuán, 2020 ) found that the bioeconomy strategy aligns with 53 targets in 12 of the 17 SDGs for the EU Member States. The authors revealed that clean energies (SDG 7), recycling (SDG 11), and ecosystem preservation (SDG 15) have positive correlations with most of the other bioeconomy-related SDGs. However, there are negative correlations between agro-biodiversity (SDG 2), domestic material consumption of biomass (SDG 8 and 12), agriculture, and industrial developments (SDG 2 and SDG 9) and a wide array of bioeconomy-related SDG indicators.

Several authors have focused on the significance of bioeconomy in achieving specific Sustainable Development Goals (SDGs). For instance, Hassan et al. ( 2019 ) highlighted the importance of biorefineries in Europe to promote cost-effective conversion of lignocellulosic biomass into bioenergy and bioproducts, thereby contributing to SDG 7 (Affordable and Clean Energy). Sadhukhan et al. ( 2018 ) explored the prospects of innovative biorefinery systems in sustainable development in Malaysia, emphasizing the advantages of extracting recyclable, metal, high-value chemicals, fuels, electricity, and bio-fertilizers from municipal solid or urban waste to achieve SDG 7. Similarly, D’Amato et al. (D'Amato et al. 2020 ) studied the principles of bioeconomy at the industry level to foster cost reduction, innovation, and competitiveness for Finnish SME companies, which could contribute to SDG 9 (Industry, Innovation, and Infrastructure). The authors concluded that SMEs play a crucial role in transitioning to bioeconomy due to their flexibility, dynamism, and capability of generating innovations. Lokko et al. ( 2018 ) explored the potential of biotechnology to transform developing nations into industrialized ones, demonstrating that bio-based industries ensure sustainability and reduce negative environmental impacts, which is relevant to SDG 9. Other studies have highlighted the significance of bioeconomy in achieving SDG 12 (Responsible Consumption and Production). For instance, Cubas et al. (Cubas, Bianchet, Reis, & Gouveia 2022 ) identified the excessive use of petroleum derivatives in cosmetics, which bioeconomy practices could mitigate. Overall, these studies show that bioeconomy can contribute significantly to achieving several SDGs, including SDG 7, SDG 9, and SDG 12.

Nevertheless, it is important to note that the bioeconomy community overlooks other SDGs, including SDG 4 Quality Education, SDG 5 Gender Equality, SDG 15 Life on Land, SDG 17 Partnerships to achieve the Goal, SDG 13 Climate Action, SDG 16 Peace and Justice Strong Institutions, and SDG 10 Reduced Inequalities, with less than 2% of publications for each. For example, Onpraphai’s study (Onpraphai et al. 2021 ) is the only one to focus on the significance of education and learning for the bioeconomy, while Baublyte’s study (Baublyte et al. 2019 ) provides a distinctive perspective by exploring the viewpoints of female leaders in the forest industry concerning gender diversity in the context of the forest-based bioeconomy approach. These findings reveal that further research is necessary to provide a more comprehensive understanding of how bioeconomy relates to the less studied SDGs. In this regard, this study provides a starting point for researchers to investigate the potential contributions of bioeconomy to achieving social SDGs, while considering the interdependencies between different goals.

The circular economy community primarily focuses on SDG 12, Responsible Consumption and Production (32.94%). In fact, the United Nations Sustainable Development Goals (SDGs) website recognizes the circular economy (CE) as a key component of knowledge resources for SDG 12. In addition, all SDGs connected with circular economy represent 13.45% of the publications under analysis, followed by SDG 9, Industry, Innovation, and Infrastructure (11.31%); SDG 7, Affordable and Clean Energy (9.55%); and SDG 6, Clean Water and Sanitation (6.43%). However, less attention has been given to the SDGs of SDG 1, No Poverty (2.14%); SDG 5, Gender Equality (0.78%); SDG 15, Life on Land (0.58%); SDG 17, Partnerships to achieve the Goal (0.58%); SDG 14, Life Below Water (0.39%); and SDG 13, Climate Action (0.19%). Strikingly, no studies were found to have focused on SDG 16, Peace and Justice Strong Institutions, and SDG 10, Reduced Inequalities.

Several studies have investigated the role of the circular economy in dealing with the challenges of achieving SDG 12 (Responsible Consumption and Production). For instance, Kenne et al. (Kenné et al. 2012 ) explored the use of production planning and control involving combined manufacturing and remanufacturing operations within a closed-loop reverse logistics network. Van Zanten et al. ( 2018 ) focused on the use of animal source food to control livestock, while Goyal et al. (Goyal et al. 2018 ) examined the alignment and management of resource flows across the value chain by integrating reverse logistics, design innovation, collaborative ecosystem, and business model innovation. Other studies have analyzed the synergies between nanotechnology and circular economy (Gottardo et al. 2021 ) and the potential of circular economy to boost innovation ecosystems and industrial sustainability (Tolstykh et al. 2020 ), which help achieve SDG 9 (Industry, Innovation and Infrastructure). Moreover, circular economy helps achieve SDG 7 (Affordable and Clean Energy) through smart and efficient energy systems (Pietrzak et al. 2022 ) and SDG 6 (Clean Water and Sanitation) using reuse of drinking water treatment sludges (Dias et al. 2021 ) and smart drip irrigation systems (Abdelzaher and Awad 2022 ). These findings are in agreement with Rodriguez et al. (Rodriguez-Anton et al. 2019 ) and Schroeder et al. (Schroeder et al. 2019 ), who asserted that CE has great potential for job creation and promotion of sustainable models, particularly in the pursuit of SDGs 6, 8, 9, 11, 12, 13, 14, and 15.

Despite the increasing interest in circular economy (CE), it is still unclear how it addresses important concepts related to Sustainable Development Goals (SDGs). While CE is commonly associated with SDG 12 — Responsible Consumption and Production, it has been found that some SDGs are underrepresented, such as those promoting economic growth and jobs (SDG 8 Decent Work and Economic Growth), eliminating poverty (SDG 1 No Poverty), improving sustainable food production (SDG 2 Zero Hunger), and improving biodiversity protection in the oceans (SDG 14 Life Below Water) and on land (SDG 15 Life on Land) (Schroeder et al. 2019 ). Limited attention has been given to some SDGs, which may hinder the development and implementation of CE policies that aim to tackle poverty (Shikwambana et al. 2021 ), gender inequality (Khalikova et al. 2021 ), and life below water (Pauna & Askham 2022 ). In fact, some CE studies question the suitability of the concept to deal with the complexities and interdependencies of sustainable development and worry about potential negative impacts on social inclusion and climate change (Sehnem et al. 2019a , b ) (Sehnem et al. 2019a , b ). Therefore, a more comprehensive and integrated approach is needed to address the broader range of SDGs and ensure that CE policies promote sustainable development.

Finally, while the bioeconomy and circular economy literature does tend to focus on some SDGs more than others, this does not mean that these are the only areas of research that are important for achieving sustainable development. It is necessary to explore the connections between the different SDGs, including those that are less researched, and how bioeconomy and circular economy can contribute to their achievement. Future research could explore the potential of green growth approaches and other environmental aspects that have not been fully considered in the literature. Overall, our study contributes to understanding the link between bioeconomy and circular economy with SDGs. However, we recognize that further research is needed to deepen our understanding of this relationship.

Literature gaps

The current study conducted a systematic literature review, which revealed various potential avenues for future research on the bioeconomy and circular economy in the context of Sustainable Development Goals. The analysis identified ten significant research gaps, presented in detail in Table 4 . These gaps encompass a range of issues, including but not limited to the need for longitudinal analyses, the dearth of regional studies, insufficient use of quantitative research methods, lack of available databases, inadequate attention to developing indicators, and the need for more research on developing countries. Identifying these research gaps highlights the potential for future studies to make essential contributions to the understanding of bioeconomy and circular economy and their relationship with sustainable development.

We have divided these gaps into four aspects:

Our systematic literature review highlights a notable research gap concerning the examination of the relationship between the bioeconomy (BE) and circular economy (CE) research communities (D'Amato et al. 2017 ; Ferreira Gregorio et al. 2018 ; Salvador et al. 2022 ). Addressing this limitation is crucial as it enhances our comprehension of how these two fields can be integrated to effectively contribute to sustainable development. In light of this, we advocate for greater collaboration and cooperation among the international scientific community working on BE and CE to address Gap 1 .

Moreover, our analysis revealed a significant oversight within the bioeconomy research field regarding the social dimensions of the Sustainable Development Goals (SDGs) (Baublyte et al. 2019 ; Tóth & Zachár, 2021 ). This finding holds great importance as it presents a challenge for the scientific community to substantiate the relevance of bioeconomy in fostering improved social conditions within countries. Addressing Gap 2 necessitates allocating research funds specifically dedicated to projects that explore the social dimensions of bioeconomy. By supporting such initiatives, the scientific community can contribute to filling this research gap and furthering our understanding of the social implications and potential benefits associated with bioeconomy.

Missing topics

The Missing Topics aspect revealed a scarcity of studies that explore the social dimensions of the Sustainable Development Goals (SDGs) (e.g., reducing inequalities, poverty, achieving zero hunger, gender, health, and education). To bridge Gap 3 and Gap 4 , international journals could consider proposing special issues and implementing other strategies to encourage a higher number of studies analyzing the interconnectedness between BE, CE, and social aspects. By undertaking these measures, the academic community can advance our understanding of the social implications and impacts of BE and CE, fostering progress towards achieving the broader objectives of sustainable development.

Additionally, our analysis revealed a notable research gap in the literature concerning the examination of bioeconomy and circular economy in developing regions. Specifically, there is a dearth of studies that assess the significance of these concepts in Latin America and Africa. Noteworthy, certain developing regions possess significant forest resources. For example, the Amazon rainforest in Brazil faces numerous environmental and social challenges (Lapola et al. 2023 ). Bridging Gap 5 requires international collaboration and cooperation between researchers from both developed and developing areas. Studies focused on developing countries hold the potential to provide valuable insights for policymakers and contribute to the advancement of sustainable development in these regions.

Our findings indicate that both the bioeconomy and circular economy research communities face challenges in accessing worldwide databases. However, it is noteworthy that the European research community benefits from the availability of the Eurostat database (Rodriguez-Anton et al. 2019 ; Ronzon and Sanjuán, 2020 ). The availability of regional data plays a crucial role in conducting empirical tests and formulating targeted regional policies. Consequently, the support of governments is vital for the development of regional databases to address Gap 6 and provide essential data for these research communities.

Furthermore, our analysis revealed a scarcity of studies focused on creating economic, social, and environmental indicators for bioeconomy and circular economy. However, the existence of regional databases would empower scientists to develop and measure these indicators effectively. Addressing Gap 7 is of utmost importance since indicators serve as critical tools for analyzing the progression of these concepts over time, making comparisons between countries or regions, and formulating policy recommendations. By tackling this research gap, researchers can enhance their ability to track and evaluate the advancements and impacts of bioeconomy and circular economy, facilitating evidence-based decision-making and policy formulation.

The research communities in bioeconomy and circular economy employ inconsistent methods, potentially impeding comparability and limiting opportunities for integrated analysis. Gap 8 highlights the scarcity of quantitative studies utilizing mathematical and statistical models to investigate both concepts, which are vital for elucidating the interplay and impact between the two. However, conducting such research necessitates the availability of regional databases, as the increasing number of quantitative studies relies on robust data sources. The lack of quantitative studies and databases also poses challenges for longitudinal studies, inhibiting the assessment of the transition and progress of bioeconomy and circular economy over time ( Gap 9 ). Furthermore, Gap 10 underscores the need for studies that analyze regional data within a country. This is particularly important for regions characterized by significant social, economic, and environmental heterogeneity, as tailored policy recommendations are required to address their unique challenges and opportunities.

In summary, this study contributes to the advancement of theory and practice in the domains of circular economy (CE) and bioeconomy (BE) through the utilization of a literature review method. By analyzing relevant articles and establishing connections with prominent research topics, we discovered limited interconnectivity between two distinct clusters, underscoring the significance of closer integration and collaboration. Additionally, our systematic literature review shed light on several limitations, including a scarcity of quantitative articles, restricted database coverage, and limited geographical representation. These findings highlight the need for further exploration in these areas, particularly with regard to social Sustainable Development Goals (SDGs).

Final remarks

In conclusion, our systematic literature review of bioeconomy and circular economy research in the context of Sustainable Development Goals has revealed several key findings and research gaps. Our study found that there is a limited number of studies that link the two clusters, bioeconomy and circular economy, and that there is a lack of databases available to researchers. Additionally, there are discrepancies in the methods adopted between the clusters. These findings suggest a need for more collaboration and standardization in research approaches between the two fields.

Furthermore, our review revealed that most of the studies focus on Europe and Asia, with a significant lack of research on developing regions. This gap is significant because bioeconomy and circular economy policies can tremendously impact developing countries, where sustainability challenges are more prominent. Therefore, researchers need to investigate the potential benefits and challenges of the bioeconomy and circular economy in these regions. Our review also highlighted a lack of attention to social aspects such as reducing inequalities, poverty, and zero hunger. Incorporating social dimensions into bioeconomy and circular economy research could help develop policies that address these issues and promote sustainable development more effectively. Additionally, our review identified a scarcity of longitudinal studies and the creation of indicators to provide policy recommendations on bioeconomy and circular economy.

This study has identified several limitations that could provide directions for future research. One limitation is associated with using bibliometric methods and keywords as the sole elements of analysis to examine the contribution of conceptual domains in different SDGs. While this approach effectively identified patterns and trends in the literature, it may not have captured all relevant studies or domains. To overcome this limitation, future studies could consider incorporating other sources of research material, such as conference proceedings, books, and book chapters, as well as using alternative methods for analysis. Another limitation is the narrow focus on scientific articles published between 2007 and 2022, which excludes potentially relevant studies outside this time frame. Expanding the scope to include a broader range of research material and a longer time horizon could provide a more comprehensive understanding of the relationship between green growth approaches and the SDGs. Moreover, the analysis did not include all environmental aspects (i.e., green growth), which is another limitation. Future research could explore alternative methods of analysis, incorporate a broader range of research material and a longer time horizon, and consider additional environmental factors to deepen our understanding of the linkages between bioeconomy and circular economy approaches and the SDGs. Such research can help to advance sustainable development agendas and promote the transition towards a more sustainable and equitable future. Finally, our systematic literature review provides an overview of the current state of research in bioeconomy and circular economy, highlighting the need for more research and collaboration to achieve Sustainable Development Goals.

Data availability

The database is public and online thought the Web of Science. Tables and analysis may be available upon request to the corresponding author.

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Ferraz, D., Pyka, A. Circular economy, bioeconomy, and sustainable development goals: a systematic literature review. Environ Sci Pollut Res (2023). https://doi.org/10.1007/s11356-023-29632-0

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