Diese Metaanalyse untersucht die Ökobilanzen (Ökobilanzen) kleiner Stromwandler, wobei ein besonderer Schwerpunkt auf Solarwechselrichtern und Wellenenergiewandlern (WEC) liegt. Die Studie überprüft systematisch 235 Ökobilanzstudien und beschränkt sich dabei auf 20 der relevantesten und vollständigsten Bewertungen. Zu den wichtigsten Ergebnissen gehört ein breites Spektrum an Treibhausgasemissionen für Solarwechselrichter, von nur 4,95 g CO2eq / kWh bis hin zu einem Höchstwert von 21,15 g CO2eq / kWh mit einem Mittelwert von 9,33 g CO2eq / kWh. Bei der WEC-Technologie reichen die Emissionen von 22,80 g CO2eq / kWh bis 143,32 g CO2eq / kWh. Die Analyse identifiziert mehrere Gründe für diese Abweichungen, wie Unterschiede in den Methoden, Systemgrenzen, geographischen Standorten, Transport, Herstellungsprozessen, Größe und Kapazität, Berechnungsmethoden und Lebensdauer. Die Studie unterstreicht die Notwendigkeit strengerer und standardisierter Ökobilanzmethoden, um vergleichbare und zuverlässige Benchmarks zu liefern. Es unterstreicht auch die Bedeutung der Konzentration auf kritische Komponenten und Phasen, wie die Herstellung von Leiterplatten und die Nutzungsphase, um die Emissionen zu reduzieren. Die Studie zielt darauf ab, diese Unterschiede zu beheben und Herstellern und Forschern bei der Entwicklung nachhaltigerer und umweltfreundlicherer Stromwandler zu helfen.
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Abstract
Purpose
The decarbonization of enabling technologies, such as power electronics converters, which supports the implementation of renewable energy, is the major research interest of this work, in a bid to produce an objective comparison of the environmental impact associated with the manufacturing and use of these technologies. This article aims to provide a comprehensive overview of the relevant literature, which has been selected on the life cycle assessment (LCA) for converters with respect to their environmental impacts, methods, and parameters, and investigates which research gaps are identified. The final goal is also to provide a first-of-its-kind background, useful for manufacturers ready to commit to sustainability, in finding current benchmarks to compare their products.
Method
A meta-analysis or systematic review method was employed in this study to set the background knowledge and relevant works, assess quality studies, and explore heterogeneity (difference between the studies and combining their effects).
Results
The results from the analysis showed a significant difference, ranging from 4.95 to 21.15gCO2eq/kWh for the solar inverters (2.5–10 kW reference size) and from 22.80 up to 143 gCO2eq/kWh for those of a WEC (wave energy converter of various sizes), with the upper values being consistently 5–6 times higher than the lower. These variations were primarily due to several factors, including (1) resource inputs and technology used; (2) geography; (3) transportation (supply chain); (4) manufacturing process/methodology; (5) sizing and capacity; (6) calculation methods; and (7) longevity. All of them have a significant impact on the results, thus potentially undermining any possible comparison unless clarifications, specific ranges, and conversion factors are specified.
Conclusion
We conclude by showing that the use of standard LCA analysis and the quality of the assumptions in the calculations would be able to reduce the uncertainties in the GHG emissions recorded in the studies, and we show how to compare studies reporting diverse data and references in a meaningful way.
Kama’an Jalo Geoffrey and Norma Anglani contributed equally to this work.
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1 Introduction
Global warming is a critical topic, and the measure of how technologies contribute to its increase has led to the consideration of renewable energy sources (RES) as an alternative to fossil fuels. The focus has been on solar PV panels with their enabling device, the power converter, which is a priority of this review. This paper deals with studies in which power converters were considered in terms of decarbonization. We aim to systematize them with the finality of paving the way for further developments toward a full understanding of the impact on the environment of this technology to the benefit of manufacturers.
Despite decades of research on power converters, there is still no definite research on assessing the entire life cycle environmental assessment (LCA) and its improvement. Benchmarks are missing in the research community, yet. Understandably, research and development are mainly focused on the reliability of the converter as an RES-enabling technology (S. Peyghami et al. 2021),Thiagarajan et al. 2019). Although the emergence of new topologies (Kar, et al. 2017, Salmon 1994) of power converters has been on the rise, these studies have not addressed the issue of decarbonization in the terms reported above. Only a few LCA have been reported. When dealing with decarbonization issues linked to power converters, most research papers focus on their reliability and efficiency in their design, and rarely on the role of materials. For example, Sarnago et al. (2014) focused on the advantages of using wide-bandgap devices to obtain less carbon-intensive materials.
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When discussing the role of components-materials, papers are more concerned about their failure rates and the quality of the components, as the internal parameter variation of the components of the power electronics is indeed always prone to failure as Sujatha and Parvathy well report in Sujatha and a.k (2019) state. The benefits of silicon carbide (SiC)-based power converters (for instance in inductive heating) have been investigated with the aim of decarbonizing the heating process: inductive heating is a direct competitor to natural gas-fed heat generation (Bac et al. 2014).
It is worth mentioning that a considerable amount of research has been carried out in the area of LCA, but it has been mostly related to what is called wave energy converter (WEC) (Thomson et al. 2011). The wind energy converter was not considered in this study because most of the work done is mainly focused on wind farms (Moussavi et al. 2023) or wind turbines without specifically focusing on the wind energy converter (Tremeac and Meunier 2009; Crawford 2007). Currently with this name, not only the sheer power converter is taken into consideration but the whole technology is. On the LCA study of Zhai et al. (2018) of a buoy-rope-drum (BRD) WEC, the performances were examined by eco-labeling its life cycle stages and processes. LCA studies of the Pelamis WEC were conducted by Parker et al. (2007) in 2007 and they showed analysis of the life cycle energy use and CO2 emissions associated. Furthermore, Thomson et al. expanded the Parker et al. analysis by including more environmental impacts (Thomson, Harrison, and Chick 2011). Apolonia and Simas also conducted research on the LCA of an oscillating WEC, called MegaRoller, with the sole aim of identifying the most important life-cycle stages of the device with respect to the respective environmental impacts. In addition, a scenario analysis was conducted to support the identification of alternatives with the least impact, considering all life cycle stages (Apolonia and Simas 2021). Therefore, from a wide environmental standpoint, power converters have mainly dealt with the role of their efficiency in reducing power loss, as in Huang et al. (2020) and Daher (2006). This article aims to provide a detailed overview of the life cycle assessment performed on the power electronics converter and the wave energy converter (WEC) in the literature thus far, and the various considerations with respect to their environmental impacts, methods, and parameters and proposes future work based on the research gap from the various literature understudied.
The remainder of this paper is organized as follows. Section 2 discusses the methodology used in the meta-analysis. An analysis of the most relevant studies on LCA of the power converter is presented with regard to the methodology used and the parameters considered. Section 3 presents a normalization and comparison of the results, and Sect. 4 presents the reasons for the variations in the results. Finally, conclusions are presented in Sect. 5.
2 Research methods and selection criteria
A systematic review explores the collection and compilation studies with a thorough and quantitative study plan (Tawfik et al. 2019). This study used systematic review (Khan et al. 2003) as the fundamental method to review the different methods in published LCA studies of solar inverters from 2006 to 2022. Meta-analysis is also applied by merging numerous scientific studies to deduce a pooled estimate closest to the fundamentals of the topic under studied (Borenstein et al. 2021). This is also one of the contributions of this work, because the selected literature may report data in a non-homogeneous manner, in terms of GHG (green-house gases) emissions. For example, one of the reviewed elements from this study is the methodology applied for the LCA studies on power converters for PV panels. Meta-analysis was first derived from the social science literature in 1976, and the term “meta” signifies the final reckoning (viz., synthesis results by counting or calculation) in Greek (Glass 1976). The meta-analysis model therefore has become the most popular in review papers due to its compelling, transparent, objective, and replicable framework (Borenstein et al. 2021). To prevent bias in the results due to scope or journal coverage (Hansen et al. 2022), more than one (1) database was used in the “Search Strategies” and the most common and important search strategy used in this study is the “keyword search.” These searches resulted in 235 studies from three (3) different databases as presented in Fig. 1. To narrow this broad base to a more robust sample, we had to filter the literature to ensure that only the most relevant and original findings were incorporated into this study. Figure 1 represents the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) which was used in the literature search, screening of papers (title and the full text screening) till the final comparison of relevant data.
Fig. 1
Detailed flow diagram guideline for systematic review and meta-analysis steps (Tawfik et al. 2019)
The methodological framework of this review encompassed two key phases: viz., phase 1: paper screening phase; and phase 2: methodology review phase. In phase 1, a preliminary search was conducted based on research questions from three different databases, and to avoid duplication, the papers were checked for duplicates since we use different databases. After the duplicate removal, a screening/filtration process commences based on some criteria as reported in Fig. 1. We characterized and analyzed the studies via a review scheme, reported in phase 2, to discover quantitative findings based on differences in methodology being used for the papers under full text review.
2.1 Paper screening phase
The process of paper screening is an essential aspect of the research process, and it is conducted in a stepwise manner. The primary objective of this process is to identify studies that fall within a predefined scope and avoid potential biases. The screening phase comprises three key stages, which are as follows: stage 1 involves conducting a search of relevant keywords (“Power converters,” “decarbonization,” “solar inverters,” and “life cycle assessment”) in databases, followed by the removal of any duplicates. Stage 2 entailed quality inspection and exclusion of articles during the title and full-text screening. The selection criteria for the articles included in this review are outlined in the process flow diagram in Fig. 2. This diagram also clearly states the rationale for the exclusion of articles.
Fig. 2
Selection criteria for choosing the best articles for the review
Articles were identified through a systematic search of the Scopus, Web of Science (WoS), and Core Collection using specific keywords. Although some of the key papers were found in ResearchGate, this was because these papers were primarily authored by industry researchers rather than academics.
2.2 Selection criteria
Here are three selection criteria, which led our sample to only 21 of the “best” studies. The following subsections describe the selection process in detail.
2.2.1 Relevance
The first selection criterion involved the removal of 87 articles based on their relevance to the research focus. These studies either failed to address life cycle assessment and/or greenhouse gas emissions of solar inverters or wave energy converters, provided insufficient information, such as total emissions for the power converter or WEC, or were not presented in a suitable format for an LCA analysis of the power converter. Although these excluded studies were generally comprehensive and competent, they primarily focused on other measures such as efficiency, effectiveness, reliability, topology, and control of converter, and often considered total costs and rates of return, total energy input, and energy payback times, as well as other environmental measures such as toxicity, carcinogen output, and water consumption, but not greenhouse gas emissions. It is worth mentioning that for Zepeda (2017), information about the GWP in terms of gCO2eq/kWh was provided, but no explanation on how they achieved the result (40 gCO2eq/kWh) is given. Table 1 presents all papers excluded based on their irrelevance to the research focus.
Table 1
Studies excluded for relevance
Keywords
Studies
Inverter/converter
(Hatanaka et al. 2015), (Wei et al. 2020), (Krebs et al. 2013), (Liu et al. 2021), (Abanda et al. 2016), (Jain et al. 2020), (el Aamri et al. 2023), (A. K. Gupta et al. 2017), (Wang et al. 2012), (Philipps et al. 2016), (Ghafoor and Munir 2015), (Zhou et al. 2021), (Haaren et al. 2014), (Thurai Raaj et al. 2022), (Bharathidasan et al. 2022)
LCA
(Scharfy et al. 2017), (Busch et al. 2017), (Al-Waeli et al. 2019), (Bordbar et al. 2023), (Kockel et al. 2022), (Rosenboom et al. 2022), (P. Sinha and A. Wade 2020), (Cassoret et al. 2019), (Nakano and Shibahara 2015), (Alexander et al. 2021), (Mattila et al. 2014), (Mansour et al. 2023), (Zhuang and Genry 2011), (González-García et al. 2009), (Mustafa et al. 2019), (Babatunde et al. 2018), (Xue et al. 2022), (García et al. 2019), (Bieda 2012), (Yamaki et al. 2023), (Nicola et al. 2008), (Arvidsson et al. 2018)
LCA + converter
(Ghodrat et al. 2017), (Navamani et al. 2021), (Liu et al. 2022), (Gottardo et al. 2023), (Vasan et al. 2014), (M. Drechsel et al. 2014)
LCA + renewable (wind, wave, solar)
(Schreiber et al. 2019), (Sinha et al. 2013), (Kaddoura et al. 2020), (Yamaki et al. 2020)
Converter (WEC, wind energy, thermal)
(Tan and Mohamad-Saleh 2023), (Galparsoro Iza et al. 2021), (Ifaei et al. 2017), (Debusschere et al. 2007), (Thies et al. 2012)
Life cycle
(Hernández et al. 2019), (Tasneem et al. 2015), (Branca et al. 2019), (Segura et al. 2017), (Al-Zoubi et al. 2021), (Koltun et al. 2016)
2.2.2 Time representative
The second criterion examined the period during which the research was conducted, focusing on the period within the target window. This resulted in the exclusion of eight (8) articles, because the scope of the study did not extend before 2006. The selection process considered significant advancements that have occurred in the field of converter design and efficiency over the past decade. While this approach may have unintentionally limited the scope of the studies to a seventeen (17)-year period, it was deemed necessary to ensure relevance and currency. All papers excluded for recentness are presented in Table 2.
The final criterion used for screening the literature was failure to consider the entire range of emissions, which led to the exclusion of seventeen (17) articles. Although these articles satisfied the previous requirements, they only aimed to quantify the GHG emissions for the life cycle impact assessment of a PV system without considering the GHG emissions of the converter or the WEC, which is part of the balance of system (BOS) for the PV. To evaluate the entirety of the global warming potential of the converter, and thus the GHGs, this study focused on the entire range of emissions. Consequently, these articles were excluded from the study. Table 3 provides the details of the articles excluded based on this criterion.
The methodological review phase is a vital component of this meta-analysis as it primarily focuses on the diverse methodologies utilized in the articles. Of particular interest is the LCA methodology detailed in Fig. 3, which is prominently featured in this phase and used as a major criterion for this analysis.
The globally accepted approach for assessing the environmental impact of a product or service is the life cycle assessment (LCA) method. According to the International Standard Organization (ISO), as reported in International Organization for Standardization 2010, p. 14,040, and International Organization for Standardization 2010, p. 14,044, this methodology considers all stages of the product/service/process lifecycle, as described by Thomson et al. (2011). LCA involves systematically evaluating energy consumption, resource usage, and pollutant emissions at each stage of the product life cycle, from raw material extraction to decommissioning and disposal. To achieve reliable and credible LCA results, it is recommended to use Product Category Rules (PCR) relevant to the product category of interest, in addition to the guidelines provided in the ISO standard series. PCR outlines the criteria and guidelines for inclusion in each phase of the assessment (Jalo et al. 2023). In the absence of a PCR for the product under study, another standard that provides appropriate guidelines for conducting a life cycle assessment for construction products, such as EN 15804, should be followed (European Committee for Standardization 2021). It is crucial to adhere to the LCA framework proposed in ISO 14040 (illustrated in Fig. 3), which provides a step-by-step method for conducting LCA analysis.
3 Results comparison from relevant papers
From the 235 examined articles, we selected 18 articles (corresponding to 20 studies), and the results were based on the most pertinent papers concerning greenhouse gas emissions, represented in terms of CO2eq. We present the outcomes of our findings based on diverse methodologies (the Impact Assessment Method (IMA)), the system boundary considered in the study, the software used, the data source, and the reference ISO. We collected other information, such as the device name for the wave energy converter (WEC), size, lifespan, and GHG emissions (gCO2eq/kWh). It is worth mentioning that the energy in the denominator of the ratio is represented by the allegedly converted energy for the lifetime of the device.
After eliminating over 80% of the studies based on the three selection criteria, 20 studies remained that were recent, relevant, and provided estimates of total GHG emissions while incorporating all greenhouse gases. These studies were then segregated into those examining WEC (11) on various device sizes and solar inverters (7) at lower power sizes.
3.1 The wave energy converters case study
The WEC results are represented in Table 4. The Pelamis WEC was analyzed in two separate studies by Parker et al. (2007) and Thomson et al. (2019). Despite using the same WEC with a rated power of 750 kW situated in the UK, the studies produced differing results for GHG emissions. This variation can be attributed to the data source and impact assessment method employed. The data source from Parker et al. (2007) was the Inventory of Carbon and Energy (an embodied carbon database for building materials), while the method for calculating embodied energy was unclear because of numerous uncertainties. The study only considered carbon dioxide emissions and calculated the annual electricity produced with the same carbon intensity over the entire lifetime.
Table 4
Summary of the qualified studies for the WEC (3 kW–10 MW)
Additionally, Parker et al. (2007) used the end-of-life (EoL) approximation method to estimate the impacts of recycling steel by assuming that all input steel was primary steel and granting credit at the disposal stage to avoid the impacts of using primary steel. It is important to note that the EoL approximation method is no longer recommended for attributional LCA, according to Thomson et al. (2019). These discrepancies in data sources and impact assessment methods contributed to the divergent GHG emissions results observed in the two studies (22.8 vs 35 gCO2eq/kWh). Thomson et al. (2019) gathered data from the Ecoinvent database, which is more comprehensive than the Inventory of Carbon and Energy. For instance, Ecoinvent accounts for the impacts of capital goods and global market flow, and the ReCiPe Midpoint (v1.3) as the impact assessment method in the calculation of carbon intensity is used, thus the variation in the results.
The analysis of the Oyster WEC device conducted by Walker et al. (2011) and Karan et al. (2020) revealed discrepancies in their findings. Both studies examined devices with the same rated power (315 kW) installed in the UK, but differed in their carbon intensity. Karan et al. (2020) identified and included additional components such as the sub-sea infrastructure for fixing the Oyster to the seabed, while Walker et al. excluded the manufacturing process of the Oyster, which is a key aspect of life cycle assessment. Furthermore, Walker et al. (2011) used a different method that provided a recycling credit at the end of life, while Karan et al. (2020) used the cut-off criteria because the environmental credits due to recycling in life cycle assessment are highly debated. Without the recycling credit, applied by Walker and Howell (2011), the embodied carbon and energy of the Oyster 1 would be 35.00 gCO2eq/kWh instead of the reported 25.00gCO2eq/kWh. Walker and Howell (2011) used the Inventory of Carbon and Energy (ICE) as a data source, while Karan et al. (2020) used Ecoinvent. These three reasons account for the variation in the results between the two studies (25 vs 79 gCO2eq/kWh).
Curto et al. (2018), Pennock et al. (2022), and Uihlein (2016) considered point absorber wave energy converter devices; nevertheless, their emissions differed significantly. Although the devices recorded various nominal capacities ranging from 500 kW to 10 MW, it is important to note that the methodologies for assessing GWP vary. Pennock et al. (2022) conducted a study on a 10-MW array of a 28-point absorber wave energy converter and the study used a process-based LCA with a cradle-to-grave system boundary. Uihlein (2016) conducted an LCA on ocean energy technologies using a 20-year lifespan, which was performed on an aggregate level for tidal energy and wave energy device types (with nominal power around 500–1000 kW) rather than a single device. Their results ranged from 15 to approximately 104.5 gCO2eq/kWh.
Curto et al. (2018) on the other hand conducted a life cycle impact consideration on two different wave energy converters with the nominal power of 30 kW; the key focus of this paper was to evaluate the environmental impact of raw materials used within the systems. This study failed to discuss the impact assessment method used in their analysis, and the life cycle phases were omitted in this study.
Douziech et al. (2016) and Karan et al. (2020) provided LCA studies on an Oyster WEC device with rated power of 315 kW and 800 kW. Karan et al. (2020) provided data on two different devices with 315 kW and 800 kW rated power (with lifetimes of 15 and 20 years, respectively), and emissions of 79 gCO2eq/kWh and 57 gCO2eq/kWh. Douziech et al. 2016) deals with an 800-kW Oyster device with a lifetime of 20 years with an emission of 65.5 gCO2eq/kWh. One key reason for this discrepancy, especially for Oyster800, is the life cycle impact assessment method used. Karan used EDIP 2003, whereas Douziech used the ReCiPe2008 Midpoint.
Patrizi et al. (2019) was a study on Overtopping Breakwater for Energy Conversion (OBREC) system WEC device to evaluate the environmental impacts and benefits in terms of carbon footprint. Although the impact assessment method used was not reported, SimaPro was used in their analysis.
Lastly, Apolonia and Simas (2021), which is the most recent study on the oscillating wave surge energy converter, focus on LCA assessment to enable decision-making regarding the least carbon- and energy-intensive design choices. The system boundary is cradle-to-grave with a lifespan of 20 years. This study provided sufficient and detailed information regarding the LCA.
In summary, from the listed studies and according to the methodologies, system boundaries, assumptions, and allegedly lifetime, the GWP for the WEC ranges between 23 and 143 gCO2eq/kWh as shown in Fig. 4; however, the power of the considered devices is quite wide, ranging from 3 kW to 10 MW over varying lifetimes of 15–60 years.
Fig. 4
GHG specific impact of WEC (gCO2eq/kWh) based on various sources
The analysis of seven studies on the greenhouse gas emissions of solar inverters revealed a wider range of emissions than those reported above for the WEC. Nevertheless, it is worth mentioning that in such cases, we consider only the inverter by focusing on smaller devices that are going to be a dominant technology in the market in terms of future numbers. Moreover, at least in Europe, this is because of the widespread use of PV systems in the residential sector.
In Table 5, a summary of all important data from qualified studies on inverters is presented. We categorized each study by listing the considered size (from 2.5 to 10 kW rated power), the standard used (ISO), the IMA, the software used, the data source, the system boundary, and other relevant information used in Table 6 (see Sect. 3.2.1 for details).
°Capacity factor of 0.75 was considered from 3468 kWh/year
**The emissions were rated over the lost energy (losses: 5225.55 kWh over 25 years)
The primary data for the solar inverter were obtained from Tschumperlin and Stolz (2016), which served as the basis for several other studies on the LCA of inverters. Sibai (2020) carried out an explicit LCA on a 2.5-kW Huawei Technologies solar inverter, following a proper LCA methodology based on standards for LCA and PAS 2050 (specification for the assessment of life cycle greenhouse gas emissions for goods and services) (British Standards Institution (BSI) 2023). The result of Tschumperlin and Stolz (2016) seems to have significant strengths; however, there is no way to check the primary data from three different manufacturers in Europe. Furthermore, all three manufacturers provided varied information on the size and weight of the printed board assembly. Nevertheless, only one manufacturer specified each component mounted on the printed board assembly in terms of their size and weights. Hence, the authors assumed that all printed board assemblies are composed of the same components and differ only in total weight and size, which is not always the case. The analysis was conducted by the authors by extrapolating the data, which was based on many assumptions about the weights of the components on the PCBA. This discrepancy raises questions about the congruency of the results, as it could significantly impact GWP (reported in terms of gCO2eq/kWh). Nonetheless, this study exhibits the same strength as the study by Huawei (Sibai 2020) despite yielding varying results. Regarding transportation, Tschumperlin and Stolz (2016) calculated the transportation demand in tonnes kilometer of raw materials using standard distances.
A European (Joint Research Center) technical report by Dodd et al. (2020) analyzed the life cycle of solar inverter, PV modules, and systems. This study focused on the PV system and modules, with little attention given to the inverter. The material input for the study was obtained from Tschumperlin and Stolz (2016), but the packaging materials and end-of-life treatment of the production waste were omitted. Additionally, the MEErP tool in Tschumperlin and Stolz (2016) was used for the LCA analysis, but no identification of the most critical component in the 2.5-kW inverter was made, which still makes it difficult for manufacturers to identify hotspots. Typically, in most studies on photovoltaic (PV) systems, the inverter is reported as part of the balance of system (BOS), and as a result, few studies have focused on the life cycle phases or considered the inverter as a hot spot.
Frischknecht et al. (2020) have a report from the International Energy Agency (IEA) photovoltaic power system program that is part of the Technology Collaboration Program (TCP) helping collaborators conduct joint research projects in PV power systems application. Therefore, this study primarily focuses on the life cycle inventory of PV systems. It also provides relevant information on the country-specified average equivalent peak hours at rated power (in terms of annual yield) for our assessment.
Santoyo-Castelazo et al. (2021) introduced a novel application of a systematic parametric methodology (LCA) to evaluate the environmental impact of a 3 kWp grid-connected multi-crystalline silicon PV system. The study focuses on the entire PV system and provides a comprehensive analysis, attributing to the inverter a 10.50% weight over the total CO2eq emissions; however, it failed to mention the most critical components of the inverter, thus paving the way toward strategies for the decarbonization of its manufacturing process.
A study by Rahman et al. (2019) focused mainly on quantification of energy use and the resulting emissions. They did not provide an in-depth analysis of the life cycle phases or information on the critical components in the inverter manufacturing process. Instead, the focus was on the PV system, and the study considered a very limited system boundary: well-to-wheel, accounting only for the energy requirement for electricity generation and emissions. Despite that, the results in terms of GHG impact are the highest among all, perhaps because it was carried out in Bangladesh, a LMIC (lower middle-income country, with a very high emission factor associated with the electricity from the grid).
Recently, the Fronius (AL 2023) report, where the authors conducted an LCA on their product GEN24[Plus] inverter, reported findings based on the LCA methodology and applicable standards. This study provided more detailed findings on the LCA for this inverter, possibly because an LCA expert body conducted the analysis and partly because there is complete data for the inputs and flow processes.
From their findings, aluminum represents the largest contribution to the mass but has a lower contribution to the carbon footprint of the inverter.
From this study, we can deepen the understanding of the role of each inverter component in terms of mass and relate it to its GHG impact. As shown in Fig. 5, for a 10-kW inverter with a total mass of 23.90 kg, the capacitors represent approximately 3.20%, but are responsible for 10.50% of the carbon footprint. The semiconductors (integrated circuits and transistors) present figures that are even more striking, with a 0.10% share of the mass but 23.9% responsible for the carbon footprint. This LCA result shows that elements with a low mass can have a significant environmental influence because of the energy-intensive processes originating from upstream stages (raw material, manufacturing, refining, etc.); thus, they cannot be neglected. One weakness of this study is that there was no mention of the application of the cut-off criteria. The cases identified the use phase as the most critical phase of the inverter, with an associated emission of 63.70% of the total carbon intensity of 1307 kgCO2eq, which resonates with those of Huawei (Li Sibai 2020), as both studies were from manufacturers, and the LCA was conducted by LCA specialists.
Fig. 5
Relative contribution of a 10-kW inverter components by mass (left, in kg) and by carbon footprint (right in % kgCO2eq). Lifespan = 20 years (AL 2023)
From the results reported above, the results are quite inhomogeneous; hence, to understand how different assumptions and boundaries of the LCA affect the results and to compare them, we operated by normalizing actions (harmonization) on the available data. Table 6 reports our assessment (see Sect. 3.2.2). The results associated with the GHG emissions of solar inverters have been normalized, and gCO2eq/kWhAC has been considered as the key indicator.
The first normalization parameter across studies from different countries in the world has been the reference to the considered lifespan. Different from what EN IEC 61853–4 [35] proposes, 1 kWh of AC power output was supplied under fixed climatic conditions for 1 year, and assuming a service life of 30 years, we assumed a 15-year lifetime, with an average of 10–20, the most considered horizon across the studies.
3.2.1 Analysis of system boundaries and life cycle phases (Table 5)
The significance of system boundaries in life cycle assessment (LCA) analysis cannot be underestimated as it gives the scope of the study which enables comparison with other life cycle assessments. Cradle to gate results only account for emissions from the acquisition of raw materials to packaging, while cradle to grave takes into consideration emissions from raw material acquisition to the end of a product’s life. To conduct an accurate analysis, it is crucial to evaluate all life cycle phases. Figure 6 offers a comprehensive analysis of the various life cycle phases of a solar inverter based on all available sources (Tschumperlin and Stolz 2016; Li Sibai 2020; Frischknecht et al. 2020) and a comparison is provided. Some of the studies could not provide detailed information on their life cycle stages while others did but recorded nearly zero (0) values for some phases. The available sources performed analysis on the life cycle phases which gives a comprehensive explanation to the environmental impact at every life phase. This could mean that the raw materials were virgin materials and the distance from supplier to the manufacturing site might be large, not exempting the energy-intensive manufacturing process. Notably, for the use phase, Sibai (2020) recorded the highest value. It also reported that the use phase, accounting for 76.12% of the total emissions (1504 kgCO2eq), is the phase with the most significant impact on the inverter’s greenhouse gas emissions, differently from the distribution and the end-of-life phase. For the end-of-life phase, it is imperative to mention that both Li Sibai (2020) and Tschumperlin and Stolz (2016) report the same 0.22% share on the total emission.
Fig. 6
GWP100 comparison of small-scale converters across studies based on the life cycle phases
Also, the analysis of the life cycle phases was extensively reported in this study; however, the end-of-life phase is one that most authors tend to overlook but (Li Sibai 2020) which is an exception. They did not overlook the end-of-life phase: in Fig. 7, the share of product weight according to the disposal mode is thus reported.
Fig. 7
Share of the product weight according to different disposal mode (Li Sibai 2020)
A significant strength of this source is its cut-off criteria, the default allocation method, allowing the margin of 10% of the mass of the product being excluded from the assessment.
As shown in Fig. 8, Sibai (2020) identified the printed board circuit assembly (PCBA) production as the phase with the highest emissions, accounting for 75.23% (1132 kgCO2eq) on the total. The author believed that their study had the most accurate and comprehensive data on the life cycle analysis of the inverter, including its thoroughness and accounting for every form of transportation in the manufacturing process.
Fig. 8
Percentage breakdown of emission from the LCA of a 6-kW inverter (Li Sibai 2020)
Figure 9 illustrates the percentage breakdown of GHG emissions based on components as detailed in Tschumperlin and Stolz (2016). Integrated circuits (e.g., the PCBs) are the most critical component, with an emission percentage as high as 42%. The lowest emissions were attributed to the use of copper, differently from Li Sibai (2020), where integrated circuits account for 75% of the total emissions (see Fig. 8). This information provides manufacturers with insight on how to reduce their emissions by focusing on more sustainable materials to make integrated circuits more environmentally friendly and reduce emissions. For Rahman et al. (2019), system boundary assessment was based on the so-called well to wheel, which means that they only accounted for the energy used in manufacturing the entire PV system. Despite this limited condition, the emissions are the highest among all the studies.
Fig. 9
Percentage breakdown of the LCA result, based on components, 2.5 kW (Tschumperlin and Stolz 2016)
It is important to note that some studies did not provide data on the life cycle phases, which may be due to a lack of adherence to the proper standards (ISO 14040, ISO 14044) in their analysis.
In summary, we can conclude by highlighting that the GWP, over the lifespan throughout the studies, turned out to be quite inhomogeneous, ranging from 4.95 gCO2eq/kWh to 1504 kgCO2eq.
Since our aim is to provide a range in the GHG indicator, which is measured as for the WEC analysis, in gCO2eq/kWh, then its computation for each case needs to be carried out. The work reported in Table 6 will focus on checking how to compare those data given in a consistent way.
3.2.2 Contribution to the alignment and equalization of results (Table 6)
Although the selected studies were homogeneous in size (ranging from 2.5 to 10 kW rated power), the results in terms of CO2eq may seem uneven and diverse. Furthermore, neither the same data nor all assumptions were provided; thus, verifying their consistency has also been part of our contribution to the analysis carried out in Table 6.
It is worth mentioning the framework of Table 6, which shows in row 2 a set of letters representing the relevant parameters of our assessment, which are useful for reporting in detail to enable the reader to understand our computations.
The letters range from “a” to “h” and represent, respectively, “a” the emissions in the reported unit at its side, “b” the rated power of the inverter or the peak power of the served PV panel in kW, “c” the considered-in-the-study lifespan in years, “d” the equivalent hours at the rated/peak power (a.k.a. annual yield) in h/year, “e” the annual converted electricity in kWh/year, “f” electricity converted over the considered-in-the-study lifespan in kWh, “g” the emissions over the harmonized lifespan (15 years), and “h” the normalized emissions/carbon footprint in gCO2eq/kWh.
It should be noted that a few parameters were calculated based on the data provided in the paper, whereas others were adjusted to represent the gCO2eq/kWh units, as some papers did not present the results in such a format. A few articles reported results that could not be compared as they were given without suitable context or without an explanation of how they have been assessed. Figure 10 provides an overview of the process of validating our results on a common 15-year horizon.
Therefore, to make the results comparable, we needed to refer to their impact (carbon footprint) to the same metric, which we chose to be gCO2eq/kWh, to be comparable with the WEC. The indicated kWh is the energy on the AC side, which is converted by the device, while the emissions are what results from the LCA in terms of global CO2eq, referred only to the inverter. This value was given for a few cases in kgCO2eq, others in gCO2eq/kWh; nevertheless, in the latter case, the consistency with our metric has been harmonized over the common horizon. For each row, the original data are reported in bold.
In Sibai (Li Sibai 2020), the overall GHG emissions “a” are provided in kg CO2eq and are related to a 6-kW rated inverter “b” over a considered lifespan of 25 years “c”; additionally, the assessment is carried out by referring to the lost energy (losses = 5225.5 kWh over 25 years).
From the knowledge of terms a, b, and c (the losses and efficiency in the footnotes of Table 5), the following were assessed:
In this case, from our computation, d becomes 1161 h/year, which is expected to be between 900 and 1500 h/year, depending on the location (Frischknecht et al. 2020), to support our evaluation. Finally, the carbon footprint becomes:
The h value for Sibai was then equal to 8.64 gCO2eq/kWh. The GHG data provided by Santoyo-Castelazo et al. (2021) are equal to 47.16 gCO2eq/kWh; however, they relate to the entire PV system. Therefore, our computation was different from that reported above.
However, from the study, a 10.5% share is declared to be found for the inverter (from column 8 of Table 5): thus, the starting (and ending) point for our comparison was 4.95 gCO2eq/kWh. Nevertheless, we calculated all the intermediate parameters, as well as reporting, where needed, the parameters over the original time span of 30 years and then over 15 years. In addition to Li Sibai (2020), Tschumperlin and Stolz (2016), AL (2023), and Frischknecht et al. (2020), the GHG emissions “a” are reported in kgCO2eq, and the lifespan is different, so we had to refer to them from the (c) period to 15 years:
To compute the metric in column h, the following intermediate results were obtained:
when “f” or “e” were given, then “h,” finally computed (in gCO2eq/kWh), was assessed by Eqs. (2) and (4) and by using the relationship between “e,” “f,” and “c” (reported below).
$$f=\text{e}*\text{c or e}=\text{f}/\text{c}$$
(7)
In Tschumperlin and Stolz (2016) and Frischknecht et al. (2020), the given GWP is in kgCO2eq, over 15 years, and thus, to convert the GWP to our metric, we just had to consider that “g” = “a.” In contrast, in AL (2023), the lifespan was 20; we calculated the annual electricity production “e” and the total electricity production over the lifetime “f” through Eq. (7), and Eq. (4) became:
$$h=\text{a}*1000/\text{f}$$
(8)
The results are 9.78 (for the first two studies) and 5.94 (for the last) gCO2eq/kWh, respectively.
In Rahman, Alam, and Ahsan (2019), the emissions were originally provided for the whole PV system (a = 173.42 gCO2eq/kWh, in Table 5), and since from Santoyo-Castelazo et al. (2021) and AL (2023), a share between 10.05 and 16.6% is the weight of the inverter in terms of emissions; we averaged those values (resulting in s = 13.55%) and assessed:
$$a{\prime}=a* s$$
(9)
Once “a” is assessed, then we processed the data over 15 years instead of 10, thus obtaining an h value equal to 21.15 gCO2eq/kWh. The efficiency of the was considered in the assessment of the converted electricity.
Finally, in Dodd (2020), we assessed all intermediate parameters starting from a = h (5.52 gCO2eq/kWh) and reporting them to 15 years. Figure 11 presents the relevant studies with their GWP (in gCO2eq/kWh) harmonized along with the average value. This value turns out to be ~ 9 or ~ 7 gCO2eq/kWh if the Rahman, Alam, and Ahsan (2019) reference is excluded from the assessment, for the reasons mentioned in Sect. 3.2.
Fig. 11
GWP indicator (gCO2eq/kWh primary y-axis) harmonized through the different sources and inverter size (power in kW secondary y-axis). Frischknecht et al. (2020) data are same with Tschumperlin and Stolz (2016); thus, it was removed from the average
It is worth mentioning that recently, a study on the DC-DC buck converter by Fang et al. (2024) proposed a functional analysis-based methodology for power electronic eco-design strategies. They presented the values for the life cycle phases based on their analysis; however, they failed to record the lifespan and rated power of the DC-DC buck converter, which unfortunately made their results not comparable with those of other studies.
4 Reasons for variation in literature
This section explains several reasons for the large variation in the results in literature. These include (1) resource inputs and technology used, (2) geography, (3) transportation (supply chain), (4) manufacturing process/methodology, (5) sizing and capacity, (6) calculation methods, and (7) longevity.
4.1 Resources, input, and technologies
The raw materials and inputs required for converter manufacturing vary in literature based on physical size, design, topology, type of control, etc. With new developments in respect to decarbonization goals, there has been a lot of improvement in the type of designs, control scheme for the converter, and all these inadvertently affect the raw materials used for each fabrication/manufacturing of the converters. Intuitively, these sorts of differences alter the GHG intensity of the manufacturing and construction life cycle stages.
4.2 Geography
Emission efficiency is directly tied to geographic location with respect to the life cycle phases and the type of data used in the analysis. Geography plays a massive role in virtually all phases of the life cycle because it has an impact on the transportation of raw materials from the supplier and of the product to the end user/market; it also plays a major role in the core process/manufacturing phase, where electricity consumption depends on the electricity generation mix to the grid of that location. Tables 4 and 6 show all the papers with their geographical locations that can justify this disparity because the studies were conducted in various geographical locations across the globe.
4.3 Transportation
While transportation—a major component of the upstream activities (raw material transportation to the manufacturer)—may not appear to be a significant contributor to the GHG emissions for the PV inverters and the wave energy converters, there is still variability in the literature on this topic. WEC studies appear to have placed less emphasis on defining the GHG emissions intensity of transportation. This notable lack of emphasis is a glaring weakness of the literature. The supplier provides this information; however, most times, such information is not available; thus, assumptions were made in the calculation, which can impact the GHG results for this phase of the life cycle analysis. Tschumperlin and Stolz (2016) used standard transport distances for raw materials (approximately 100 km), whereas others assumed other values that were not reported.
Transport can vary in both upstream and downstream activities (transport from manufacturing to installation site or to the end user); therefore, it is safe to say that there are several factors that can explain this variation with respect to transportation. Finally, LCA studies focused significantly less on defining the GHG intensity of transportation, which is a clear weakness of the literature.
4.4 Manufacturing process/methodology
Fabrication and manufacturing are energy-intensive processes that may partially depend on direct fossil fuel use, mostly the electricity consumption by the individual process flows, which needs to be accounted for. All the process flows and utilities that used energy should be accounted for; however, due to difficulty in sourcing the relevant information on these electronic components, most literature make assumptions in the methodology when it comes to the electricity mix (this is highly dependent on the geographical location and the type of electricity generators available to the grid in such locations).
De Wild-Scholten et al. (2006) gave some comparisons of the environmental impact assessment method, and from Table 4, we can see that the methodology used differs (though most of them used the LCA methodology shown in Fig. 3 afnd thus the disparity). Depending on how carbon-intensive these sources are, the recorded emissions tend to differ significantly.
4.5 Capacity and size of the converter
The available sources suggest a positive relationship between the physical and nameplate capacity sizes of systems and their emission intensity. It is important to note that the size of the system can also affect the number of materials required for its fabrication or manufacturing, which in turn can affect the total greenhouse gas emissions associated with the process. For the WEC device, we can see that a 315 kW WEC had a GHG intensity of 25 gCO2eq/kWh while a 3 kW had an intensity of approximately four times higher amounting to 85.70 gCO2eq/kWh.
4.6 Calculation methods and impact assessment methods used
Finally, the life impact assessment methods used in each study were also a cause of variation. The authors from our sample relied on various life cycle impact assessment methods including the CML2001 method (named based upon its founding institution, the Centre for Environmental Studies at the University of Leiden), Recipe, TRACI (mostly used for EUROPE), IO (input–output), and International Organization of Standardization (ISO) methods. It is important to mention that in one-third of the LCA studies, the software or calculation tool was not specified which should be reported in the studies Santoyo-Castelazo et al. (2021).
Furthermore, they relied on a variety of different software, including different versions of SimaPro and GaBi, the MEErP Tool (Laurent et al. 2020), and different system boundaries and materials databases, such as the popular Ecoinvent database or the ILCD. Laurent et al. (2020) presented valuable guidelines for impact assessment interpretation, which is another key aspect connected to the method used; this is another factor for disparities.
4.7 Life span
Longevity is an obvious factor that influences GHG intensity. However, it is also imprecise because there are many unknown considerations, such as how well the converters are maintained, the physical and natural conditions at the installation site, and how quickly the installations and their interconnections degenerate.
Additionally, because most converters have not (yet) been deployed for full life spans due to the high failure rate, which has led to numerous studies on how to improve the reliability of the converters, the emissions were estimated to be 15 years or 25 years, as noted by Tschumperlin and Stolz (2016) and Li Sibai (2020) respectively even though the lifetime on the market currently seems to be closer to 10 years, but it is projected to increase.
5 Conclusion
In this study, 235 life cycle studies of GHG emissions for the converter were screened to identify a subset of the 20 most relevant, current, and complete assessments. It finds a range of emissions intensities for the inverters from as low as 4.95 gCO2eq/kWh to a high of 21.15 gCO2eq/kWh with a mean value of 9.33 gCO2eq/kWh and for the WEC technology, a low value of 22.80 gCO2eq/kWh with a high value of 143.32 gCO2eq/kWh; therefore, these 2 technologies can only be term “low carbon” rather than “carbon free” technologies based on these results.
The first and most obvious conclusion is that life cycle assessment studies of greenhouse gas emissions associated with solar inverters need to become more methodologically rigorous to be comparable and provide potential benchmarks. Of the original 235 articles, 41% failed to consider greenhouse gas emission intensity when considering life cycle impacts.
More than 50% of these were outdated, irrelevant, or are reviewed papers. Having excluded over 80% of the initial search articles, leaving only about 7% selective literature, among these articles, the system boundaries, the life cycle impact assessment methods, and interpretation were dissimilar, and many embodied varying assumptions related to a multitude of factors such as resource inputs, manufacturing and fabrication, sizing and capacity, and longevity, among others as explained in detail above.
These studies raise pressing concerns regarding the use of standard LCA analysis and reduce the assumptions in calculations, which will undoubtedly reduce the uncertainties in the GHG emissions recorded in the studies. Looking at the information provided by Tschumperlin and Stolz (2016), AL (2023), and Li Sibai (2020) on the hotspots for components, we see that it provides manufacturers with insight into how to reduce their emissions by focusing on more sustainable materials to make integrated circuits more environmentally friendly and reduce emissions. By looking at these disparities and drawing from these two conclusions, several important concepts are revealed regarding how to most effectively use the life cycle assessment of converters. It is particularly important to note that the key factor in this disparity is the methodology used in the literature and the life cycle impact assessment method used. Some of these methods depend on the geographical location of the analysis.
One reason why Rahman et al. (2019) studies look like outliers could be the fact that they accounted for electricity production in a developing country and the study was based on a well-to-wheel assessment not the traditional cradle-to-grave. This shows the significant impact of electricity on manufacturing processes.
Lastly, the LCA studies focused significantly less on defining the GHG intensity of transportation, which is a clear weakness of the literature.
5.1 Future research and limitations
With these disparities in mind, we suggest that a more harmonious methodology should be widely and unanimously used by both industry and academic researchers to close the gap existing in research and in determining the true emissions from any product or system. We recommend that both institutions inculcate the use of various standards for every analysis conducted and adhere to the cut-off criteria set by the ISO standard. Additionally, the GHG protocol should be a major part of the adhered standards when conducting LCA studies of products and services.
Declarations
Conflict of interest
The authors declare no competing interests.
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