Geolocation for Low-Carbon Dunaliella salina-Based Biorefineries with Valorization of Industrial Exhaust Gases

Abstract

The utilization of carbon dioxide (CO₂) from industrial emissions as an input in microalgal biorefineries represents an integrated strategy that contributes to mitigating and transforming residual resources into value-added products. The valorization of CO₂ from gaseous effluents through biotechnological routes also contributes to the development of a bio-based circular economy. This article aims to present the carbon footprint of a microalgal biorefinery system with CO₂ recovery from exhaust gases for the 193 countries of the world. The results reveal that the tons of carbon dioxide equivalent (tCO₂e) emissions of the proposed biorefinery system can be as low as 3 tCO₂e per year and as high as 590 tCO₂e per year. Countries with emissions greater than 445.98 tCO₂e per year were considered, following a statistical approach, as having low environmental performance in terms of the implementation of the proposed technology. This study’s insights help establish benchmarks for the implementation of microalgal biorefineries that are more capable of recovering industrial emissions—environmentally.

1. Introduction

Phenomena such as glacier retreat, sea level rise, biodiversity loss, and the frequency of extreme weather events have intensified across all continents [1]. These phenomena are driven by a complex network of interactions, with greenhouse gas emissions, especially those of CO₂, as the primary driving force [2]. Projections suggest that net CO₂ emissions could reach ~50 Gt/year by 2050, potentially raising the global average temperature by up to 6.1 °C—well above the 1.5 °C target by 2100 [3,4]. Given this outlook, it is crucial to understand the processes responsible for such emissions, including the leading emitting countries—China, the United States, and India—and the most impactful sectors, such as energy and heat production, transportation, and infrastructure [5,6,7]. However, while identifying priority countries and sectors is important, it is equally vital to recognize the transboundary nature of the climate crisis and the urgent need for coordinated action to mitigate its interconnected effects, which impact the social, economic, and environmental nexus.

In this scenario, it is primordial to consider all opportunities for mitigating or adapting emissions. Carbon capture and utilization (CCU) technologies constitute an important foundation for the recovery and incorporation of CO₂ into processes and products. In CCU physicochemical routes, CO₂ can be separated from the exhaust gas mixture through (i) solvent absorption, (ii) physical adsorption, (iii) membrane separation, and (iv) cryogenic distillation and is subsequently used in the production of chemicals and materials [8]. In the biological route, called biological carbon capture and utilization (BCCU), photosynthetic organisms, such as microalgae, directly assimilate the CO₂ present in gaseous effluents, converting it into biomass or metabolites of interest. This strategy, in particular, represents a promising approach to creating economic value from harmful gaseous effluents [9]. Furthermore, the concept of a microalgal biorefinery, integrated with the utilization of industrial waste, has been proposed as an alternative not only for producing a diverse range of renewable products but also as a strategy for environmental management [10].

Microalgal biorefineries are integrated systems designed to fully utilize biomass by dividing it into multiple products. These systems can also be combined with phycoremediation processes [11,12]. The opportunity for integrated and circular processes provided by bio-based biorefineries gives them a strategic role in combating climate change. However, in the state of the art, it is important to note that there is no reference to a multi-product microalgal biorefinery unit on a relevant commercial scale. But focusing on high-value products such as pigments and polyunsaturated fatty acids as the core of biorefineries in the short and medium terms is a key strategy to ensure their viability [13]. At the same time, shifting from fossil to renewable energy sources in bioprocesses is crucial to overcoming the environmental deficit associated with microalgae-based systems [14,15]. Additionally, integrating cultivation with residual flows, such as wastewater and exhaust gases, provides these systems with the circularity needed to align with the principles of the bio-based circular economy, promoting resource efficiency and a potential reduction in emissions [16].

In this context, research groups have evaluated scenarios for integrating exhaust gases for the cultivation of these cell factories [17,18,19,20]. Of note, microalgae generally require CO₂ supplementation to optimize their growth, especially in large-scale cultivations, which represents an opportunity to harness these gases as a carbon source. However, although studies show that some microalgal strains can tolerate and grow in the presence of all components of these gases, which include, in addition to CO₂, carbon monoxide (CO), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) [21,22,23], the low cell yields observed—especially under uncontrolled conditions and with variable gas compositions—have hindered the successful consolidation of this integration [24]. Furthermore, although microalgae generally tolerate and utilize the CO₂ found in exhaust gases well, their tolerance to other components is low, and they often create an inhospitable environment for microalgae [25].

In this narrative, the use of physicochemical CCU routes designed to selectively capture CO₂ from exhaust gases, although a viable alternative, increases the already high energy demand of upstream and downstream microalgal processes [26]. Although some studies include the carbon footprint related to this recovery and incorporation of CO₂ into the life cycle of microalgae-based products [27], no research, so far, has comprehensively and comparatively evaluated the impact of this integration on the environmental footprint of microalgal biorefineries, considering energy matrix scenarios worldwide. This gap is particularly important because energy-intensive systems, like microalgae-based ones, need to be located in regions with cleaner electricity grids to promote a low-carbon circular economy [13].

In this context, the unprecedented contribution of this work is to provide, for the first time, a georeferenced environmental benchmark of the carbon footprint of a microalgal biorefinery system that incorporates CO₂ from exhaust gases, considering the electricity matrices of the 193 countries in the world. To this end, Dunaliella salina was chosen as a model organism due to its recognized ability to accumulate β-carotene in high concentrations, its robustness to adverse environmental conditions, and its widespread use in microalgal biorefinery studies, making it a benchmark species in this field. Additionally, based on a statistical approach applied to the data obtained, a statistical limit was established for the carbon dioxide equivalent (CO₂e) emissions associated with the biorefinery system, allowing countries to be classified in relation to the implementation of microalgae-based biorefineries with exhaust gas valorization into three categories: high, moderate, and low environmental performance potential. The results presented in this study, which is limited to environmental sustainability, integrate the effort aimed at overcoming the current fossil and linear economic model, whose practices often perpetuate waste and inefficiency.

2. Materials and Methods

Life Cycle Assessment (LCA)

The LCA performed according to the ISO 14040 [28] series of the International Organization for Standardization (ISO 14040) included four phases: (i) goal and scope definition, (ii) inventory analysis, (iii) impact assessment, and (iv) interpretation of the results [28]. The objective of the LCA was to assess the carbon footprint of a biorefinery system based on Dunaliella salina with the valorization of an industrial residue—CO₂—considering the scenarios of the energy mix of each country in the world, as well as the scenarios grouped by countries’ income classifications according to the World Bank (low-income, lower-middle-, upper-middle-, and high-income countries) (Figure 1). The scope of the LCA covers the carbon emissions associated with the electricity consumption of the upstream and downstream processes of the complete biorefinery system. The system boundaries were defined from the cradle-to-gate life cycle. This study considered the joint production of 1.0 t year⁻¹ of β-carotene (main product), 1.51 t year⁻¹ of bulk oil (secondary product), and 2.34 t year⁻¹ of defatted biomass (tertiary product) as a functional unit. Considering an operational time of 330 days and an average cultivation cycle of 15 days, approximately 22 batches per year were estimated for the cultivation of Dunaliella salina. The proposed unit operations for producing β-carotene from the microalgae Dunaliella salina in a raceway pond align with the production process outlined in references [29,30,31,32]. After cultivation in a raceway pond, the Dunaliella salina biomass is harvested, dried, and used to extract the pigment β-carotene with supercritical carbon dioxide. The residual biomass from the pigment extraction is then subjected to a hexane-based lipid extraction process. The mixture of biomass and solvent is centrifuged, and the solvent is recovered. The oil-rich extract is sent to a desolventizer, obtaining bulk oil. Finally, the residual biomass from oil extraction is used to obtain defatted biomass through the desolventizer–toaster–dryer–cooler, as described by dos Santos et al. [33]. The data for theoretical and experimental analyses were obtained from Dias et al. [13], Kunitomi et al. [26], and Deprá et al. [29]. Data on the electricity mix for each country and for each country group classified based on income were obtained from the Our World in Data (OWID) [34]. Note that references [13,26,34] were chosen for their direct relevance to the conceptual framework of the adopted biorefinery model and the construction of the global electricity mix scenarios. Both are considered central to understanding the logic presented in this study. The layout of the system boundaries is shown in Figure 2.

The life cycle inventory data of the Dunaliella salina-based biorefinery system with CO₂ valorization from industrial exhaust gases is detailed in

Table 1. The inputs and outputs of the microalgae-based biorefinery system. Adapted from Refs. [13,26,29].

Process	Unit	Base Case
Cultivation
Raceway pond	m3	63.2
Production days	days	330
Batches per year	b year−1	22
Energy for paddle wheel	kWh	270,122.7
Energy for water pumping	kWh	50,022.7
Energy for CO2 recovery from exhaust gases	kWh	13,549.0
Energy for CO2 injection	kWh	165,075.0
Water evaporation	m3	8.2
CO2 consumption	tCO2 t−1 dry biomass	2.0
Biomass productivity	t m3 year−1	1.5 × 10−1
Output
Algal liquid	t	50.0
Harvest
Input
Energy consumption centrifugation	kWh	1894.8
Drying
Input
Wet biomass	t	12.5
Spray-dryer	kWh	13,632.3
Output
Dry biomass	t	9.4
Pigment extraction
Input
Dry biomass	t	9.4
sCO2	kWh	7504.0
Output
β-carotene pigment	t	1.0
Bulk oil production
Input
Residual biomass	t	8.4
Extractor	kWh	594.5
Energy consumption centrifugation	kWh	875.2
Solvent recuperation	kWh	1037.7
Evaporation/Stripper	kWh	194.1
Desolventizer	kWh	328.2
Output
Bulk oil	t	1.51
Defatted biomass production
Residual biomass	t	6.7
Desolventizer–toaster–dryer–cooler	kWh	922.7
Defatted biomass	t	2.34
Total energy requirement	kWh year−1	525,753.0

Acronyms: sCO₂, supercritical CO₂ extraction.

. The CO₂ demand for Dunaliella salina cultivation corresponds to the stoichiometric demand of 2 kg CO₂/kg of dry biomass, which represents the mass of CO₂ absorbed by the microalgae during photosynthesis. This demand is based on the net mitigation potential, i.e., it considers the CO₂ effectively removed from the atmosphere through incorporation into the biomass. In this study, based on data reported by Kunitomi et al. [26], the vacuum temperature swing adsorption (VTSA) method combined with N₂ sweeping was adopted as the technique used to recover CO₂ from exhaust gases because it exhibits high recovery efficiency (~90%) and low energy consumption (2.6 GJ/t-CO₂) and provides a gas with adequate purity, with impurities below 1%, conditions compatible with the cultivation of Dunaliella salina [26]. In VTSA, the CO₂ source considered was a typical exhaust gas from methane combustion. CO₂ adsorption was assumed to be performed with 13X zeolite in a shell-and-tube adsorber. It is worth noting that the composition of exhaust gases from liquefied natural gas (LNG) combustion is very similar and, in this case, can be considered an equivalent alternative. The recovered gas is a mixture of CO₂ and nitrogen (N₂). N₂ is inert in the cultivation of Dunaliella salina. In this study, it is worth highlighting that the model does not differentiate whether the recovery process occurs in situ or ex situ; it is only assumed that the recovered CO₂ is available for the cultivation of Dunaliella salina.

The global warming potential (GWP) was the indicator used to quantify the carbon footprint of the Dunaliella salina-based biorefinery system with the valorization of CO₂ from industrial emissions. The GWP was manually calculated, as generically described in Equation (1), based on the ReCiPe 2016 method (Midpoint, World–Hierarchist version) [35], directly adjusting the Ecoinvent database, accessed through Microsoft Excel files. To estimate the GWP, initially, the relative share of each electricity source in each country’s electricity matrix was identified. Using these proportions, it was possible to determine how much of the energy consumed in the life cycle of the Dunaliella salina-based biorefinery corresponded to each electricity source. These values were then multiplied by the respective emission factors (kg CO₂e/kWh), and the results were summed to obtain the annual CO₂e emissions associated with the proposed biorefinery system in each country.

$$GWP=\sum CF\times E_i$$

(1)

where GWP is the global warming potential, CF is the emission factor, and E_i is the emission inventory, expressed in mass released into the environment per functional unit (tCO₂e year⁻¹). The specific CFs by electricity source are listed in the Supplementary Materials (Table S1), while the E_i value corresponds to the total energy requirement (kWh year⁻¹) presented in Table 1.

Additionally, to classify countries based on the environmental performance potential of implementing a microalgae-based biorefinery system with the recovery of exhaust gas emissions, a statistical approach based on the distribution of the emission data themselves (tCO₂e year⁻¹) was used. Since there is no universally established absolute emission limit for the environmental performance potential, a relative comparison approach was adopted based on the data distribution itself, in line with recommended practices in exploratory and geoenvironmental studies based on LCA. Countries were categorized into three groups based on the quartiles of the distribution according to environmental performance potential: high (emissions ≤ first quartile, Q1 = 150.55 tCO₂e year⁻¹), moderate (emissions between the first and third quartiles, Q1 < x ≤ Q3, with Q3 = 445.98 tCO₂e year⁻¹), and low (emissions > Q3). This method avoids the imposition of arbitrary thresholds and allows for relative interpretations, helping to prioritize regions with greater potential for environmental sustainability.

The target audience for this study includes the parties involved and interested in promoting the bio-based economy and microalgal biorefineries using CO₂ from industrial exhaust gases.

3. Results and Discussion

The environmental life cycle inventory provides detailed information on the inputs and outputs of the Dunaliella salina biorefinery system with CO₂ valorization from exhaust gases. This information helps map the biorefinery system’s critical points (Figure 3). Using the inventory data, it can be observed that, based on energy demand, the main critical point is the energy requirement for the paddle wheels (51.4%). This high consumption is associated with the fact that the continuous mixing and circulation of the culture require the constant operation of these devices to ensure adequate mass transfer. The literature, based on both previous studies [36] and more recent investigations [37], confirms that the energy requirement for the paddle wheels consistently ranks among the most energy-intensive processes in microalgal cultivation in raceway ponds, being repeatedly highlighted as one of the main bottlenecks in these systems. In addition to the paddle wheels, the energy requirements for CO₂ injection (31.4%), water pumping (9.5%), spray-dryer drying (2.6%), CO₂ recovery from exhaust gases (2.6%), and pigment extraction by sCO₂ (1.4%) are also critical [38,39]. The CO₂ recovery from exhaust gases for use in Dunaliella salina cultivation, with a recovery rate of 90%, showed a limited contribution to the energy demand of the biorefinery. Although the strategies used in the technology of CO₂ recovery from exhaust gases, such as operational adjustments in the VTSA cycle, for example, reducing the time or flow rate of the sweep gas, or thermal integration with other stages of the plant [26,40], can contribute to reducing the energy demand of the CO₂ recovery process and, consequently, of the Dunaliella salina biorefinery, it is worth highlighting that they lack, however, specific validation for applications in microalgal systems.

Regarding the energy requirements for centrifugation, bulk oil recovery, and defatted biomass, it is concluded that they also had limited contributions to the total energy footprint of the biorefinery system with CO₂ valorization from exhaust gases. Furthermore, the energy requirements for bulk oil recovery (secondary product) and defatted biomass recovery (tertiary product)—the biorefinery by-products—suggest that the conceptual framework of the Dunaliella salina biorefinery is positioned to maximize value and minimize waste. Studies such as those by Xi et al. [41] and Nguyen et al. [42] show that Dunaliella salina yields a high amount of carotenoid-rich biomass, particularly β-carotene (~10% dry weight), significantly enhancing the technical, economic, and environmental feasibility of its application in biorefineries. Additionally, recent studies, including those by Monte et al. [43], Rodrigues Dias et al. [13], and Dutta et al. [44], demonstrate that integrated processes for producing target compounds and by-products are technically viable and advantageous. In other words, coupling the recovery of by-products to the pigment production phase, as presented in this work, eliminates the need for extra inputs (nutrients and energy) and repetitive operations (cultivation, harvesting, and drying) that would be necessary if each product were produced independently. Thus, the existing data and the concept of the microalgal biorefinery itself—grounded in technological integration—support the fact that the proposed structure indeed aligns with the principles of value maximization and loss minimization in biorefining systems. However, as is known, a commercial reference for microalgae-based biorefineries has not been established, but many projects seek to unlock the potential of this approach by increasing the production scale and reducing costs, prioritizing the production of high-value products before low-value ones, following the cascade principle [10,45,46,47,48].

Additionally, it is important to mention here that the microalgal cultivation system directly affects the efficiency of carbon fixation. Studies like Duarte et al.’s [49] show that the highest CO₂ fixation rates are achieved in closed systems, mainly because of their geometry, which favors gas residence time and its dissolution in the cultivation medium. However, although closed systems such as tubular photobioreactors generally have higher CO₂ utilization rates compared to open systems, they often consume more energy to operate. This greater use of energy—especially in contexts with electricity grids based on fossil sources—incorporates indirect emissions that can annul the benefits arising from the greater carbon fixation capacity. Thus, the choice of an open system, such as shallow lagoons without mixing or raceway ponds mixed with paddle wheels that are typical of the large-scale production of Dunaliella salina, may involve a strategic trade-off, ruled on the search for a balance between technical–economic and technical–environmental performance. Therefore, while the current work did not directly compare the CO₂ fixation efficiency of different cultivation systems or their impact within the biorefinery framework for CO₂ valorization from exhaust gases, it is worth noting that some studies, such as Deprá et al.’s [29], have explored this variable as a key factor in optimizing environmental performance in microalgae-based systems.

Figure 4 shows the CO₂e emissions for a Dunaliella salina biorefinery with CO₂ valorization from exhaust gases based on a joint functional unit of 1 ton of β-carotene, 1.51 tons of bulk oil, and 2.34 tons of defatted biomass per year under the electricity mix scenarios of the 193 countries of the world. CO₂e emissions from the biorefinery system are as low as 3 tCO₂e year⁻¹ and as high as 590 tCO₂e year⁻¹. This variation observed in CO₂e emissions is directly related to the composition of each country’s electricity mix, being proportional to the carbon intensity of the energy matrix used—the greater the participation of fossil sources, the greater the emissions associated with the biorefinery system (Table S2).

The literature still lacks direct references that enable a consistent interpretation of the CO₂e emissions from the biorefinery system proposed in this study. However, relative comparisons can be made based on similar studies. For example, Deprá et al. [50] reported that β-carotene production from microalgae presents average values of 852.49 tCO₂e per ton of product, which is higher than the maximum of 593.69 tCO₂e obtained in this study for producing 1 ton of β-carotene and its co-products, considering the Botswana electricity mix. For comparison purposes, the same authors indicate that synthetic β-carotene production results in emissions of approximately 118.22 tCO₂e per ton of product. Although the biological production of β-carotene generally results in higher emissions compared to synthetic methods, it is important to note that, depending on the electrical mix used, the value of 118.22 tCO₂e falls within the range estimated in this study (2.84 to 593.69 tCO₂e year⁻¹; Figure 4).

Additionally, studies that examine different electrical mixes provide complementary perspectives. Lopes et al. [51], for example, reported emissions of approximately 36.58 kg CO₂e·kg⁻¹ of dry biomass when using flue gas as a carbon source, which was reduced to 10.6 kg CO₂e kg⁻¹ of dry biomass in an optimized scenario with water recirculation. In this study, using the same electrical mix—the Portuguese electricity mix—the estimated emissions were approximately 15.71 kg CO₂e kg⁻¹ of dry biomass, which falls within the range reported by Lopes et al. [51]. Similarly, López-Herrada et al. [52] demonstrated that producing a microalgae-based fungicide using a biorefinery approach with the Spanish electrical mix would result in 46.92 tCO₂e year⁻¹ (totaling 703.91 tCO₂e over 15 years). Amponsah et al. [53], in turn, observed that the production of packaging material in a marine biorefinery system, using the UK electrical mix, would imply emissions of around 41.52 tCO₂e per tonne of biopolymer.

Although these studies consider different products, they show that the values obtained in the present study (2.84 to 593.69 tCO₂e year⁻¹) fall within the range reported for microalgae-based biorefinery systems. It is worth noting, however, that differences in scope between studies—including the products and co-products considered in the functional unit and the specifics of national energy matrices—make direct comparisons challenging. But it is observed that many recent studies have left aside the adoption of scenarios based on a single source of electrical energy, such as coal or another specific source, and have started to consider local and regional electricity mixes, which more realistically reflect the sustainability of microalgal systems [54,55,56,57,58].

In summary, the countries that contribute the lowest CO₂e emissions from the Dunaliella salina biorefinery system, such as Bhutan, Nepal, Democratic Republic of the Congo, Ethiopia, Lesotho, Central African Republic, Paraguay, Iceland, Norway, and Costa Rica, have in common a predominantly renewable and clean electricity matrix or environmental policies that encourage green technologies or a low level of industrialization—as observed in African countries. On the other hand, the countries that contribute the highest emissions from the biorefinery system, such as Indonesia, Kazakhstan, Poland, India, South Africa, Kosovo, and Botswana, share an abundance of fossil resources and the energy infrastructure based on these resources.

In parallel, when grouping countries according to the World Bank income classification, it is observed that lower-middle-income countries (413.83 tCO₂e year⁻¹) and upper-middle-income countries (360.79 tCO₂e year⁻¹) are the ones that contribute the most to emissions from the Dunaliella salina biorefinery system. High-income countries contribute 238.11 tCO₂e year⁻¹, and low-income countries contribute 128.19 tCO₂e year⁻¹. These data indicate that the higher emissions from the proposed system are not necessarily associated with the largest global economies. This may reflect the broader adoption of renewable and clean energy matrices or more consolidated environmental policies in these nations. The higher emissions observed in middle-income countries may be linked to increasing industrialization and the use of an electricity matrix still heavily dependent on fossil fuels, as reported by Huo et al. [59] and Bi et al. [60]. On the other hand, it is worth noting that the low emission levels observed in low-income countries may not mean a high degree of sustainability. Instead, this data may reveal structural limitations and a lack of resources or industrial development—which requires, in any case, a more careful and sensitive analysis of the sociopolitical context of these countries to avoid reductionist interpretations.

Additionally, from another perspective, Figure 5 shows a visual analysis that categorizes countries according to their environmental performance potential—high, moderate, or low—for implementing a Dunaliella salina-based biorefinery system with CO₂ valorization from exhaust gases. The countries labeled with high environmental performance potential are mainly in South America, North America, Western Europe, and parts of Central and East Africa. These regions have the lowest emission levels related to the evaluated system, indicating greater potential for the sustainable adoption of the proposed technology. Conversely, countries considered to have low environmental performance potential are mostly in Central Asia, South Asia, Southeast Asia, and parts of Southern Africa, reflecting the highest estimated emission levels. The countries classified as having moderate environmental performance cover a large part of the world, showing intermediate potential where improving performance depends on adjustments to achieve superior environmental sustainability.

In general, it is notable that while some countries lead in renewable energy generation due to the predominance of these resources and technologies built based on these sources, others live in the shadow of infrastructures consistently established based on fossil resources. There are also those located in remote regions that, despite having a favorable climate for renewable energy generation and microalgal production, have limited logistics infrastructure and restricted access to consumer markets. At the same time, countries with favorable infrastructure and climate but that are far from robust consumer markets, such as North America, end up being less competitive due to the cost attributed to the complexity of the logistics of the transport and distribution of products. In this sense, it is clear that looking only at the electricity matrix of countries and their potential to create biorefinery systems with CO₂ recovery from lower-carbon exhaust gases is only the tip of the iceberg. Thus, it is important to emphasize that this study is a primary assessment of countries whose electricity matrix would help to sustain energy-intensive facilities in a more environmentally friendly way.

Finally, the focus of this study was on global warming potential as the only environmental impact category to provide an initial analysis centered on greenhouse gas emissions. Although other categories are also relevant in the context of microalgal biorefineries, they were not addressed in this initial analysis. Future studies may benefit from a multi-category approach that also incorporates cost–benefit analyses and practical optimization strategies to make microalgal biorefineries with exhaust gas valorization more technoeconomically and technoenvironmentally viable. Furthermore, in this study, the fraction of CO₂ lost to the atmosphere, resulting from low gas–liquid transfer efficiency in open systems, was not included in the modeling. It is recognized that this choice may underestimate the real CO₂ consumption level and that future work can incorporate this variable to provide more conservative and realistic estimates of the carbon footprint associated with the Dunaliella salina-based biorefinery system with exhaust gas valorization. The impacts related to building and shutting down biorefinery infrastructure, as well as the supply and transport of nutrients (fertilizer) and the use and reuse of water—which can significantly impact the environmental performance of the system in certain regions—were also not included. Additionally, the assumptions about carbon recycling technology, especially regarding its integration with biological systems like microalgal cultures, were based on small-scale experimental data. The absence of validation at an industrial scale limits the generalization of these results and highlights the need for future research to evaluate the technical, economic, and environmental performance of this integration in real-world operational settings. Furthermore, uncertainties in the inventory data—such as energy demand and estimated productivity—may affect the results, especially when extrapolated to different geographic and climate conditions, which is another limitation of this study. However, despite these limitations, this research provides a valuable contribution by providing a global-scale georeferenced assessment of the carbon footprint of a microalgal biorefinery system with CO₂ recovery from exhaust gases, serving as rapid and valuable feedback for investors and researchers to take a step forward in building a low-carbon legacy in energy-intensive industries.

4. Conclusions

This study showed that emissions from a Dunaliella salina biorefinery with CO₂ recovery range from 3 to 590 tCO₂e year⁻¹, depending on the national electricity matrix, reinforcing the decisive role of geolocation in environmental performance. The results provide a georeferenced environmental benchmark that can support decision-making in the contexts of decarbonization, energy transition, and circular bioeconomy.

However, it is important to recognize that, although energy plays a central role, economic, climate, and logistical factors also influence the viability of these systems in different regions and should be considered in future analyses.

Finally, the transition to clean and renewable energy infrastructures is a key element for green and sustainable technologies to become truly transformative and competitive. Without this foundation, initiatives like the microalgae-based biorefineries remain tied to fossil fuels and may even lose their environmental legitimacy.