Analyzing the Potential Environmental and Socio-Economic Impacts of Regional Energy Integration Scenarios of a Bio-Based Industrial Network

Abstract

The goal of this work was to evaluate the socio-economic and environmental life cycle advantages of alternative defossilization pathways for a bio-based industrial network in Central Germany. Five scenarios were defined considering the potential energy utilization of further regionally available biomass capacities. The evaluation was made using an integrated approach, i.e., using a traditional life cycle assessment methodology, complemented by a regionalized socio-economic life cycle assessment framework. The results show that the environmental advantages from the change in energy provisioning reduced about 5% of the total environmental impacts. The analysis of the socio-economic impacts shows that the path to defossilization of the integrated network provides a clear enhancement of the expected regional socio-economic impacts. These results show that 100% decoupling from natural gas brings overall positive sustainability advantages to all organizations participating in the evaluated value chains. The methodological approach presented in this work can allow the identification of hotspots and opportunities within the regions where the implementation of technological alternatives takes place.

1. Introduction

Biomass, as the main resource of the bioeconomy, has the potential to help transform the present fossil-based economy into a renewable resources-based industry, although there are a few challenges, such as local availability and the conceptualization of regionalized strategies, in making the use of biomass resources economic and sustainably realizable [1,2,3]. The European Commission reports on the demand for a systemic perspective for the regional deployment of bioeconomy in order to enhance innovation on a regional level [4]. Thus, there is a need for linkages between different science disciplines and sectors and to implement value chains into regional value cycles for the sustainable management of regional resources [5,6,7]. For this purpose, it is essential to collaborate not only within one particular sector, but also between clusters, economic sectors, providers of technological solutions and services, as well as regions and stakeholder groups representing societies to foster the bioeconomy on regional levels [8,9]. Previous works have indeed shown that the regional integration of bio-based production systems to promote the internal exchange of heat and by-products, and through this, implementing concepts of eco-industrial parks, is an important asset for a successful industrial symbiosis [10,11,12].

In addition, it is a common notion that within the implementation of technological innovations, social acceptance by end-users is needed. Increased public awareness and improved communication have been identified as needed actions. These include traceability and transparency of supply chains and the implementation of product certifications or labels on bio-based products in order to foster sustainable consumption patterns [13]. In this sense, previous works have addressed the environmental and social advantages of regional industrial integration in the bioeconomy field when optimizing resource management [14,15]. They have also shown that sustainability is not a given aspect of the bioeconomy; on the contrary, they show that a concerted effort must be established in order to achieve the sustainability advantages from a systemic perspective [16,17,18,19].

Considering the above, this work aims at evaluating the socio-economic and environmental life cycle advantages of the full defossilization of an already planned, environmentally and technically optimized bioeconomy network, using the available regional biomass capacities. For the substitution of natural gas, the scenarios pay attention to resource potentials which can be mobilized for solid energy carriers such as bark and waste wood, as well as for refining biomethane from household and industrial waste water treatment plants (WWTP) (with different carbon loads). Considering the critical scientific discourse on how to enhance the framing and relevance of life cycle assessment approaches for the transformation of the regional industry system towards a future bioeconomy system, we highlight possible approaches to overcome common shortcomings, as well as the limitations of LCA-sLCA approaches.

2. Materials and Methods

2.1. Goal Statement for the Integrated, Regionalized sLCA-eLCA Approach

The sustainability assessment of bio-based industry networks within a regional context requires that we (i) set a well-defined scope of the study with a relevant system boundary, (ii) collect inventory data of socio-economic and environmental performance of bio-based production systems, (iii) define a holistic functional unit for the regional basket of bio-based products and (iv) qualify realistic assessment scenarios for future developments within the regional production system.

The goals of this assessment study are to assess the functional unit of a regional basket of bio-based products, perform a comparison of different scenarios for energy integration within the industry networks, analyze the social and environmental performance of different scenarios and determine how process energy can be delivered for a bio-based industry network.

The assessment approach which is applied to reach these goals is based on the integration of an environmental LCA approach and an sLCA method in line with a scenario-based evaluation and scenario parameter variation.

2.2. Scope of the Case Study Assessment and Potential Integration Pathways within the Region

The regional focus of the system boundary of the case study assessment is a central European region, Central Germany, including parts of the states of Thuringia, Saxony and Saxony-Anhalt.

The supply area for forest resources has a radius of around 150 km around the “Südharz” region, from which coniferous and deciduous wood resources are mobilized. The wood resources include wood from stem wood assortments as well as pulp and fiber wood assortments, and they are harvested mainly in sustainably managed forests within the resource catchment radius. The production sites are assumed to be located 20 to 120 km from each other. The input resources include forest resources, as described, as well as fossil resources such as natural gas, and within the system boundary, the increased valorization of by-products and residues and waste flows is assessed considering the potential for biomethane generation as a replacement for expensive and GHG-intensive natural gas resources imported from outside the case study region.

The first methodological step of the assessment was the definition of a case study within the capacities of the region. As Figure 1 shows, a mix of coniferous and beech wood was chosen as the resource basis, which also defines the functional unit of the integrated industrial network. As already established in previous works [10], the selection of this resource basis was realized considering the availability of wood resources in Central Germany, which contains ca. 40% of the beech wood resources in Germany.

The selection of the technological approaches for the production of the envisaged bio-based goods corresponds to technical developments with a technology readiness level (TRL) of at least 6, meaning that the technology has been demonstrated in an actual system application [10,20].

For establishing the socio-economic and environmental potential impacts of the energetic regional integration, four scenarios were defined (see

Table 1. Definition of scenarios.

Scenario	Types of Energy Carriers	Source of Origin
Scenario 0: Not decoupled from natural gas	Natural gas, bark, industrial wood residues
Scenario 1: 25% decoupled from natural gas	Natural gas, bark, industrial wood residues
Scenario 2: 50% decoupled from natural gas	Natural gas, biomethane, bark, industrial wood residues, waste wood	Fermentation stillage, sewage sludge, LVL production, glulam production, CLT production, waste wood from external waste systems
Scenario 3: 75% decoupled from natural gas	Biomethane from sewage sludge, corn, biowaste; bark, industrial wood residues, waste wood	Fermentation stillage, sewage sludge, LVL production, glulam production, CLT production, waste wood from external waste systems
Scenario 4: 100% decoupled from natural gas	Biomethane from sewage sludge, corn, biowaste; bark, industrial wood residues, waste wood	Fermentation stillage, sewage sludge, LVL production, glulam production, CLT production, waste wood from external waste systems

). They were set as bioenergy utilization pathways that consider the sequential integration of further available bio-based resources in the region as decoupling from fossil carriers’ perspective.

2.3. Life Cycle Modeling

The most relevant prerequisite for setting up a robust LCA model (Figure 2) is to construct the model relying on an inventory analysis of up-to-date practical data of wood-based bioeconomy processes and on realistic future scenarios and the most probable ex-ante scenario definition for process integration and process modeling. Considering up-to-date practical data, the life cycle modeling relied on data gathering and aggregation of conversion factors collected in the leading-edge cluster bioeconomy and included allocation factors for by-product valorization [21,22].

When specifying the life cycle model for engineered wood products, the water and energy balances for wood and fiber drying and the waste formation and utilization factors for saw mill by-products and for clippings had to be included, which were retrieved from the existing previous works [22,23,24,25,26,27].

On the energy side, the demand for process energy in the production systems of wood manufacturing such as heat for wood drying, fiber drying and electricity for pressing had to be cumulated [28,29].

When specifying the life cycle model for the lignocellulosic biorefinery, the demands for steam, heat and electricity were aggregated and relied on the life cycle modeling of previous works in the case study region [30,31,32,33]. The modeling of the fractionation of wood for producing lignin and lignin derivatives was based on the process developments and findings from the Lead-Edge Cluster Bioeconomy and especially from the works of CBP Leuna [34]. The definition of product alternatives and the specification of process data as life cycle inventory data for the manufacturing of wood fiber-based and lignin-based composite materials was based on findings from R&D and implementation activities conducted in the context of the Leading Edge Cluster in Central Germany [35,36,37].

The overall modeling of a multi-output product system was set up by more detailed analysis and further scenario specifications and parameter variations included in models established by the authors [21]. The individual LCA models for the assessment of the environmental impacts of engineered wood products, of bio-based resins and chemicals, of lignin-based resins and of fiber-reinforced composites were integrated into an overarching LCA model for the entire bio-based industry network.

From the input side, the fossil-based fuel consumption was known, and for substituting these fossil inputs, the specific efficiency ratios for energy provisioning from biogenic energy carriers were used.

Table 2. Information sources for the defined processes in the case study region.

Bio-Based Processes	Database or Source
Wood fiber insulation boards EU-27: Methylene diphenyl diisocyanate ((p)MDI) Transport of wood chips Transport of paraffin Refinery process Fiber drying Curing of adhesive Cutting and packaging Diammonium phosphate, as P2O5 RER: Wood chips, hard wood RER: Wood chips, soft wood RER: Paraffin, at factory gate	Gabi database, Sphera, 2022 Communications and site visits with Holzimpulszentrum Rottleberode and FH Rosenheim Own modeling [37] [25]
Cross-laminated wall elements Transport, truck Gluing <e-ep> CLT press <e-ep> Diesel, at regional storage Melamine–urea–formaldehyde resin, at factory gate Sawn timber, hardwood, planed, chamber dried, Sawn timber, softwood, planed, chamber dried	Own modeling Communications and site visits with HIZ = Holzimpulszentrum Rottleberode and FH Rosenheim [27,37]
Biorefinery Concept 3.2 (cradle-to-gate, aggregated), hydrolysis lignin Biorefinery Concept 3.2 (cradle-to-gate, aggregated) Biorefinery Concept 3.2 (cradle-to-gate, aggregated), lignin Flax long fibers PE Kenaf fibers, at farm Thermal press <e-ep> Prepreg production Foaming of NFK-PU elements Transport, freight overseas	[32] [32] [31] Communications with Fraunhofer IWM 2014 [38] [27] [34] (Unkelbach et al. 2009)
Substitution of natural gas: Biomethane from maize silage from maize silage; production mix, at producer; 74.9% carbon content, 48.9 MJ/kg net calori., EU-28; agg Biomethane from municipal organic waste from municipal organic waste; production mix, at producer; 74.9% carbon content, 48.9 MJ/kg net calori. Biomethane from sewage sludge from sewage sludge; production mix, at producer; 74.9% carbon content, 48.9 MJ/kg net calori.EU-28 Agg Biomethane from Biorefinery Concept 3.2 (cradle-to-gate, aggregated), hydrolysis lignin	[38] [32]

provides a summary of the information sources that were used for modeling the case study.

The defossilization of the bio-based industry network was modeled assessing four different scenarios progressing towards full substitution of fossil energy carriers such as natural gas with biogenic energy carriers including biomethane, bark firing and waste wood firing. The datasets used for modeling the energy provisioning systems were adapted from the GaBi database [38].

The life cycle impact assessment (LCIA) was conducted as a final step and assesses the potential to reduce environmental impacts on the basis of 18 ReCiPe 1.08 midpoint (E) indicators. These indicators are aggregated on the basis of three endpoint indicators and take into account one of the three cultural perspectives, suggested by the ReCiPe methodology, which in this case is the cultural perspective of Egalitarian (E), offering the most long-term perspective of aggregation and reflecting the precautionary principle (please compare to [39]).

2.4. Regional Social Life Cycle Modeling

For the evaluation of the socio-economic impacts of the value-added chain, the sLCA model RESPONSA was applied, as proposed by Siebert et al. [40] and modified by Bezama et al. [41].

2.4.1. Goal and Scope

The goal of the social life cycle assessment activity was to identify the potential regional socio-economic hot spots and opportunities for regional integration by assessing the organizations involved in the assessed value chains. In this particular assessment, three major stakeholder groups are addressed: workers, the local community and society, following the methodology set by Siebert et al. [40].

In order to ensure a better alignment for the integrated assessment, the functional unit was defined commonly with the environmental life cycle assessment, i.e., by introducing the product basket associated with the case study region, without introducing an activity variable. The socio-economic assessment was therefore performed considering the potential impacts of all the organizations found along the studied value chains. In this sense, considering the definition of the case study system presented in Figure 2, a total of 10 organizations were identified, ranging from the upstream resource extraction processes to the final products described in Figure 1.

Organization 1 (O1) corresponds to the forest operations and logistics management located in the Federal State of Thuringia. Organization 2 (O2) corresponds to a saw mill in Central Germany. The datasets for the characterization of O1 and O2 were acquired from previous studies [42]. In order to complete the organizational value chain, a series of facilities were identified to be participants in the case study. Organization 3 (O3) corresponds to a panel production facility. Organization 4 (O4) is a composite manufacturer facility. Organization 5 (O5) is a biorefinery, and Organization 6 (O6) is a facility that produces engineered wood products. For characterizing O3–O6, the social archetypes constructed based on the RESPONSA database, following the methodology proposed by [40,43], were utilized, considering the regional scale of Central Germany.

In addition, Organizations 7 and 8 correspond to the producers of resins and adhesives, as well as the energy distributors in the region. Finally, Organization 9 corresponds to a sewage sludge facility, a provider of biomethane, and Organization 10 corresponds to a electricity generator from biomass, also located in Central Germany. In the case of these organizations, the social archetypes from the RESPONSA database were used for characterizing their socio-economic performance.

2.4.2. Inventory Analysis

For this case study, following the suggestions by Bezama et al. [41], a set of 15 indicators were selected from the RESPONSA dataset, thus feeding a total of six indexes and covering two stakeholder categories: workers and the local community. The set of selected indicators is shown in

Table 3. Selected indicators for the RESPONSA modeling as adopted by Bezama et al. [41].

Index	Indicator	Unit	Description	Indicator ID
Sub-Index
1. Health and safety
Sick-leave	Preventive health measures	Cat.	Health measures (e.g., sick leave analysis, health activities)	I1.1
2. Adequate remuneration
Payment	Payment according to basic wage Average remuneration level	y/n EUR	Payment of basic wage Average payment per month per full-time employee per total employees	I2.1 I2.2
3. Adequate working time
Working time	Contractual working hours	h	Average contractual working hours per week per full-time employee	I3.1
Work-life-balance	Access to flexible working time agreements Rate of part-time employees	% %	Percentage of employees with access to flexible working agreements Number of part-time employees per total employees	I3.2 I3.3
4. Employment
Job conditions	Rate of qualified employees Rate of marginal employees (max. EUR 450)	% %	Percentage of employees with professional training per total employees Percentage of employees earning max. EUR 450 per total employees	I4.1 I4.2
Duration of employment	Rate of fixed-term employees Rate of employees provided by temporary work agencies	% %	Number of fixed-term employees in relation to total employees Number of employees provided by temporary work agencies per total employees	I4.3 I4.4
5. Knowledge capital
On-the-job training	Employees/unity participated in training Support for professional qualification	% y/n	(Qualified) employees/unity participated in training per total employees Assumption of cost or exemption for training programs	I5.1 I5.2
Vocational training	Rate of vocational trainees	%	Trainees/total employees	I5.3
6. Equal opportunities
Gender equality	Rate of female employees in management positions Rate of female employees	% %	Percentage of female employees in management positions in relation to all employees in management positions Percentage of female employees in relation to total employees	I6.1 I6.2
Legend for Units: Nr: Number, Cat.: Category, %: Percent, y/n: yes and no, h: hours

.

This selection was realized due to constraints on the data availability of the proposed indicators, and for the opportunity to construct regional social archetypes of the participating organizations from the available information. In this way, it was possible to include information for defining the potential socio-economic characteristics of the organizations participating in the planned value chains.

2.5. Limitations of the Study

The scenarios for environmental life cycle assessment are not yet coupled to an economic modeling approach by means of a life cycle costing (LCC) approach, which assesses the resource availability and fuel costs of limited bioenergy carriers for process energy provisioning. Furthermore, in the future, other gaseous energy carriers such as hydrogen and liquid synfuels such as methanol could be upscaled in the region. Scenarios for the availability of these renewable fuels are also excluded from the actual assessment. Moreover, increasing super-regional competition for waste wood resources is not reflected in the study, but in further studies, these aspects must be addressed in order to estimate more accurately the economic effects as well as the potential impacts on the availability of the resources for the regional bio-based industrial systems.

Regarding the regional socio-economic assessment, the major limitation of the study is the lack of primary data of the organizations that will take part in the envisaged value chains of the case study. As indicated in Section 2.5, this lack of information has been addressed through the introduction of “socio-economic archetypes” of the participant organizations. These social archetypes are prepared for each organization type using the same available official data that the RESPONSA database is constructed from, using an average regional value considering the economic sector in which the organization is classified. This ensures that the results of the study can represent the potential effects on a regional scale. However, it must be clear that a more accurate estimation can only be carried out when the value chains are actually established and their participant organizations provide their primary information.

3. Results

3.1. Environmental Life Cycle Assessment

Figure 3 shows the results of the life cycle impact assessment (LCIA) for the five scenarios (0 to IV) considering the enhancement and/or worsening of reducing environmental impacts among the set of 18 ReCiPe 1.08 midpoint (E) impact categories aggregated on the basis of the three endpoint indicators from the cultural perspective of Egalitarian (E): long-term based on the precautionary principle. The most significant reductions in negative environmental impacts associated with the fuel switch away from natural gas in process energy provisioning can be observed for the impact categories ReCiPe 1.08 Endpoint (E)—Climate change, human health and ReCiPe 1.08 Endpoint (E)—Climate change, Ecosystem.

Alongside the reduction in fossil fuel input progressing throughout the four scenarios, the climate change impacts are reduced by around 4%. However, an increase in negative environmental impacts is also seen when comparing the four scenarios and increasing the share of biogenic energy carriers.

In particular, the results for the categories ReCiPe 1.08 Endpoint (E)—Freshwater eutrophication, Fine particulate matter formation and Fresh water ecotoxicity showed an increase in negative impacts of around four to five percent compared to the baseline scenario.

3.2. Results of the Social Life Cycle Assessment

Figure 4 shows the characterization values for the evaluated organizations participating in the organizations assessed in this case study. In addition, the scores of each indicator as a result of the RESPONSA methodology are presented in this table. By averaging the individual indicators corresponding to each sub-index, it is possible to obtain an overview of the hotspots in the value chain.

It can be observed that the socio-economic performance of the value chain is rather low. In particular, Figure 4 shows that the indexes “health and safety”, “knowledge capital” and “equal opportunities” score relatively low in comparison to their regional performance reference points.

It is through the regional integration for defossilization that the whole network starts to enhance the socio-economic performance. As observed in Figure 5, this integration allows the overall score of the six evaluated indexes to be enhanced, achieving an acceptable value in all indexes in Scenario 4. This allows us to suggest that further regional integration allows for a more socio-economically competitive region, which is reflected in the number and quality of jobs associated with the regional network. It is also possible to estimate that the indices are affected for better regional competitiveness, which could be a major factor when planning the actual implementation of the value chains in the region, as it may positively affect the existing industrial infrastructures.

4. Discussion

Based on these preliminary results, the implementation of an integrated network in the bioeconomy field does bring a series of environmental advantages. In the case study, the environmental advantages from a fuel switch in energy provisioning observed as a result of the scenario analysis are quite limited, showing a reduction of around 5% in the total environmental impacts. This situation was nonetheless expected, as the wood industry already has a high level of process energy integration in place. Moreover, this is because the main share of environmental impacts are either (i) more associated with resins and adhesives, which are not that easy to substitute in the medium term, or (ii) are highly related to land use in forestry and agriculture and eutrophication and acidification, which are substantially reduced but rather compromised by the provisioning of biogenic energy carriers. However, for economic competitiveness reasons and for climate protection reasons, the fuel switch towards the full defossilization of process energy provisioning is a highly relevant mid-term target.

However, it is in the analysis of the socio-economic impacts where the results show more interesting features. The most relevant one is that the path to defossilization of the integrated network implies further integration within the region, which implies the additional integration of organizations working in the bioeconomy sector. This further integration, even though it deals with a small part of the total mass, implies the improvement of the overall socio-economic conditions in the region.

In addition, when evaluating the scenarios in an integrated way (Figure 6), it is possible to observe an optimization path that is achieved through the regional integration in terms of the potential environmental and socio-economic life cycle impacts. This shows that the path for regional defossilization shows progress in the right direction in terms of both its socio-economic and environmental impacts.

It can be observed that the results of the successive integration proposed by the defined scenarios follow an interesting optimization pathway. Nonetheless, this optimization seems on the one hand quite limited, but also too linear. On the other hand, it shows that the optimization pathway associated with the defossilization of the energy provision system of the case study region is pointing in the right direction, although it will require a major transformation of energy provisioning in order to achieve overall favorable socio-economic conditions.

The linearity of the results in the integrated approach addressed above can be explained by the setting of the scenarios. In this regard, the results would differ if we alternated between bio-methane and bark wood residues, whose utilization ratio remained always constant. In this sense, a more thorough analysis could be amended by introducing sub-scenarios for determining the impacts associated with the introduction of different utilization rates of the alternative biomass sources. In addition to the feedstock feed ratio, a system expansion for avoiding this linearity could be introduced, looking deeper into the energy carriers. This will be investigated in subsequent work.

On the other hand, it is important to mention at this point that these results must be considered as preliminary ones. Nonetheless, the obtained results on the assessed socio-economic indicators can be aligned to the findings of previous works in the bioeconomy field [44,45,46]. This is despite the fact that these works are dealing with the evaluation of value chains on a more global scale, not focusing on the regional perspective as this present work does. The inclusion of a regionalized sLCA approach to complement the more traditional sLCA assessments can be seen as an interesting future development.

Moreover, it is expected that these potential results can be further refined by the implementation of primary data coming from the regional organizations that can be potential actors in the value chains involved in this case study. Another important issue is the need to include further socio-economic stakeholder groups in the discussion, which is also a recurring aspect in the state-of-the-art sLCA works dealing with value chains that are not circumscribed to a regional context [45]. Thus far, the regional socio-economic life cycle assessment has been carried out by considering three stakeholder groups (i.e., workers, local community and national community) in the assessment. In order to align the analysis to the current sLCA guidelines, we would need to incorporate the analysis of the stakeholder groups of government and value chain actors, which is currently a work in progress.

The market aspects and the constraints on resource availability and associated prices, as well as the energy availability, are not included in this analysis, and will be considered in future studies.

5. Conclusions

The ex-ante evaluation of intertwined trade-offs, synergies and optimization constraints of socio-economic and environmental performance of different transformation scenario trajectories for regionally integrated bioeconomy systems is highly important for proper decision making in regional business development, regional economic planning and for regional policy advice for decision makers in different counties and states.

Our results show that for the case study system, 100% decoupling from natural gas does not only improve the ecological effects of the energy supply, but is also related to the social improvement potential of the overall value chain, which is a positive result also for the individual companies participating in it.

A combination of life cycle approaches such as the one presented in this work can allow us to identify a wider range of hotspots and opportunities for the implementation of technological alternatives. This can therefore provide a more solid decision basis for regional authorities and investors when deciding on the implementation of technological alternatives.