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• DOI: 10.1080/23311886.2024.2354976
• Corpus ID: 270236497

Criteria and methods in nuclear power plants siting: a systematic literature review

• H. Susiati , M. A. Widiawaty , +3 authors Khairul Handono
• Published in Cogent Social Sciences 3 June 2024
• Environmental Science, Engineering, Political Science

85 References

Identification of river ecosystem services through water utilization at merapi volcano, indonesia, landscape dynamics and its related factors in the citarum river basin: a comparison of three algorithms with multivariate analysis, the flood management policy in bandung city: challenges and potential strategies, public policy communication for flood management, good for the planet, good for the wallet: the esg impact on financial performance in india, utilizing satellite imagery for seasonal trophic analysis in the freshwater reservoir, globalization, renewable energy consumption and sustainable development, advanced nuclear energy: the safest and most renewable clean energy, nexus between macroeconomic uncertainty, oil prices, and exports: evidence from quantile-on-quantile regression approach, multivariate analysis and modeling of shoreline changes using geospatial data, related papers.

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Direct and indirect health effects of the nuclear power plant disasters: a review for health care professionals

Okano, Ichiro MD a,b ; Rosenberg, Ashley MD c ; Dworkin, Myles MD d ; Murthy, Vijayashree MD a ; Jayaraman, Sudha MD, MSc e ; Takabe, Kazuaki MD, PhD, FACS a,f,g

a Department of Surgical Oncology, Roswell Park Cancer Institute, Buffalo, NY

b Department of Orthopedic Surgery, Showa University, School of Medicine, Tokyo, Japan

c Department of Surgery, Virginia Commonwealth University School of Medicine, Richmond, VA

d Department of Orthopedic Surgery, Thomas Jefferson School of Medicine, Philadelphia, PA

e Division of General Surgery and Center for Global Surgery, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT

f Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo the State University of New York, Buffalo, NY

g Department of Breast Surgery, Fukushima Medical University School of Medicine, Fukushima, Japan

This manuscript has been peer reviewed.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 10 August 2022

* Corresponding author. Address: Breast Surgery, Department of Surgical Oncology, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263. Tel: (716) 845-5128; fax: (716) 845-5705. E-mail address: [email protected] (K. Takabe).

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

Background: 

Nuclear power plants are deeply integrated into our society. They possess substantial risk for major disasters. Two of the worst-categorized nuclear power plant disasters were Chernobyl and Fukushima, emitting large amounts of radioactive materials and required mass evacuations in neighboring areas.

Methods: 

This is a rapid review of the literature. We searched PUBMED and Medline for original studies of all large nuclear power plant disaster information documented in literature.

Results: 

Eighty-three publications were identified in the review. The results are summarized in categories based on direct health effects such as immediate health effects, indirect health effects related to evacuation, cancer, behavioral effects and environmental effects like proliferation of wildlife and other infectious diseases.

Conclusion: 

Nuclear power plant disasters have a great impact on human health including increased rates of cancer, behavioral and psychosocial problems, and evacuation related problems. These disasters can lead to major environmental impact, specifically on wildlife, resulting in unanticipated health consequences for local populations. In this review, we describe these consequences of nuclear power plant disasters as they apply to local health care workers.

There are ~450 nuclear power plants currently operating worldwide with 50 more under construction 1 . There are 118 plants operating in North America, 99 in the United States and 19 in Canada, accounting for 20% of the total electric energy in both countries 2 . The United States generates the greatest amount of electricity from nuclear plants in the world, 50% more than the second leading country France 1 . Until 2011, Japan generated ~30% of its electricity from nuclear reactors 3 . This decreased to ~2% after the 2011 tsunami triggered the Fukushima nuclear accident and shifted the public’s opinion against nuclear power 4 .

Nuclear power is an essential source of modern energy but has a potential risk for mass disaster. The International Atomic Energy Agency (IAEA) classifies nuclear accidents into groups from 1 to 7, with 1 being benign anomalies and 7 being Major Accidents resulting in massive release of radioactive material 5 ( Fig. 1 ). The worst nuclear accident to occur in the United States, the Three Mile Island, in Pennsylvania is classified as group 5 or an “Accident with Wider Consequences.” Two examples of group 7 or “Major Accidents” are events that occurred at Chernobyl, Ukraine, and Fukushima, Japan.

“Major Accidents” require urgent mass evacuation of people in affected areas and are associated with long-term radioactive contamination of surrounding areas. In contrast, the Three Mile Island accident (group 5) involved emission of gaseous radioactive nuclei with relatively short half-life without heavy metal nuclei. This meant that ~195,000 people evacuated within a 20-mile radius, were able to return to their homes within a few weeks 6 . The “Major Accidents” (group 7) in Chernobyl and Fukushima were more extreme, essentially permanently displacing over 250,000 people in Chernobyl since 1986 and 150,000 in Fukushima since 2011 7 . As of January 1, 2018, ~370 km 2 around the nuclear plant remains designated as an evacuation area due to the ongoing issues with radiation exposure 8 ( Fig. 2 ).

The direct health effects of these disasters have been extensively investigated over the last decade 9–11 . In addition, there have been reports about secondary health problems caused by evacuation such as outbreaks of communicable diseases, mental stress, and cardiovascular diseases 12,13 . In this review, we briefly summarize the effects of chronic radiation exposure associated with a nuclear plant disaster, secondary health-related issues caused by evacuation, and the influence of disaster-modified ecosystems after nuclear accidents which can be useful for health care workers in disaster environments.

On January 26, 2018, the search was conducted on electronic databases (PubMed and Medline) using the keyword “large nuclear power plant disaster.” Inclusion criteria was original articles and government agency reports that focused on radiation and health effects after Fukushima disaster in early 2011, and the papers that full text was accessible. Focus was placed on immediate health effects that affected humans following a nuclear disaster as well as long-term effects. Effects on wildlife population and environment were also included. Total of 144 publications were identified with the keyword, where 35 were excluded because they were published before 2011, 16 were excluded because they were review articles, and 10 were excluded because only the abstracts were accessible.

Results and discussions

Eighty-three publications were identified in the review. The results are summarized in categories based on direct health effects such as direct immediate health effects, indirect health effects such as related to evacuation, cancer, behavioral, and psychosocial effects and indirect health effects such as proliferation of wildlife and effect on local human populations and other infectious diseases.

Direct health effects related to nuclear power plant disasters

Immediate health effects.

The effects of ionizing radiation on human health through occupational and medical radiation exposure, as well as atomic bomb survivors, has been investigated and reported by many studies 14–16 . Short-term health effect of radiation is known as “acute radiation syndrome.” This involves a cutaneous radiation injury defined as local skin injury at the irradiated or contaminated body parts 17,18 , and a systemic response caused by cell damage in multiple organ systems. Acute radiation syndrome primarily affects employees at nuclear plants or those who live near highly radioactive sites. However, the bigger health concern amongst the general population and rescue workers following a nuclear plant disaster are the long-term effects of radiation exposure, including cancer, behavioral and psycho-social issues and health problems related to evacuation 19,20 .

Radiation exposure causes health problems through either exposure or contamination. Radiation “exposure” is defined as receiving energy of ionized radiation waves or particles from external radioactive sources 16 . If the dose is large, delivered in a short time and penetrates the entire body, it has the potential to result in acute radiation syndrome. This occurred in the past in survivors of Hiroshima and Nagasaki atomic bombs and first responders to the Chernobyl Nuclear Power Plant disaster 21 . “Contamination” is defined as extended direct contact with radioactive material, which may be via taking material into the body (internal contamination) or placing them on the skin (external contamination) 22 . Internal contamination often goes unnoticed and has the potential to lead to chronic exposure following a nuclear accident 23 . Both exposure and contamination cause health problems when the radiation dose is high. For survivors of atomic bombs and workers in highly radioactive environments, the effect of radiation exposure is the main concern 24,25 . Meanwhile, internal contamination is the primary concern for the general population surrounding nuclear plant accidents 26 .

Long-term health effects

Health effects related to evacuation. Evacuation imparts significant health risk on patients. Previous reports have demonstrated that people in temporary disaster shelters are at risk for various acute medical conditions like deep vein thrombosis 27,28 , respiratory infections 29,30 , viral enterocolitis 31 , and dehydration 32 . Evacuation is also associated with the onset of chronic illness including hypertension 33 , hyperlipidemia 34 , diabetes mellitus 35 , metabolic syndrome 36 , chronic kidney disease 37 , liver dysfunction 38 , and obesity 39 , as well as further deterioration of various chronic illnesses 34,40 . For elderly people, the stress of evacuation can lead to overall decrease of function 41 and even death. Hasegawa et al 13 reported that more than 50 elderly people living in hospitals or nursing facilities died of hypothermia, exacerbation of medical illnesses or dehydration, during the evacuation of the Fukushima nuclear disaster, even though there was no direct radiation injuries reported.

Cancer. There is a high prevalence of cancer amongst individuals exposed to high doses of radiation and further, the likelihood of cancer is correlated with the dose of radiation 14,24,25,42 . Meanwhile, patients subject to internal contamination usually are exposed to lower radiation doses for extended periods. This makes estimation of disease likelihood more complex, due to the minor cell and DNA damage repair mechanism 43,44 and the elimination of radioactive materials from organ systems 44–48 .

In Chernobyl, a higher incidence of cancer has been observed 20 years after the accident, among children and adolescents who were exposed to radioactive fallout 48 . Radioactive fallout contains a number of potentially harmful nuclei including Iodine (I-131) and Cesium (Cs-134 and Cs-137). Radioactive Iodine (I-131) commonly accumulates in thyroid tissue leading to thyroid cancer. Some researchers suggest that this may be in part due to low baseline iodine intake in affected area, which make individuals more vulnerable to radioactive iodine 49–51 . It is estimated that more than 6000 cases of thyroid cancer of all ages were caused by the Chernobyl disaster 52 . In Fukushima, radiation doses to the public were lower than at Chernobyl. Thyroid screening for children and adolescents under 18 has been conducted. In a 4-year continuous survey involving 300,476 people, 113 cases of thyroid cancer have been identified, most of which are thought to be unrelated to radiation exposure 53 . Coordinated efforts are underway to avoid overdiagnosis that may result from the use of highly sensitive ultrasound equipment and examination protocols which can cause its own health consequences. Furthermore, they also stated the results of thyroid screening in distant areas in Japan were similar to the result of Fukushima 54 .

The other radioactive nuclei for concern in general population are Cs-134 and Cs-137. These were released largely after Chernobyl and Fukushima and have long half-lives. Radioactive Cs is primarily deposited in muscle tissue in previous animal studies 46,55,56 . The data of low-level internal contamination with radioactive cesium for cancer occurrence is limited. It is reported that slight but statistically significant dose-correlated increase of cancer incidence was observed among people who lived in radioactive cesium contaminated areas in Sweden after the Chernobyl accident 57 . Regarding Fukushima, no report has been conducted regarding the relation between radioactive cesium contamination and cancer incidence, but continuous monitoring is needed. High incidence of nonthyroid malignant solid organ tumors, has been noted up to 10 years following radiation exposure 14 .

Behavioral and psycho-social health effects. Radioactive cesium has been reported to accumulate in neural tissues 58 . The relation between contamination and cognitive impairment or other neurological disorder is still controversial. Reports have shown that clean-up workers in Chernobyl and people living in highly contaminated areas have a higher incidence of difficulty sleeping, depression, anxiety, posttraumatic stress disorder, and medically unexplained somatic symptoms 59 . It is very difficult to determine whether the etiology of these symptoms are related to radiation exposure or purely psychological. Behavioral and psycho-social consequences are common after any mass disaster event 60–62 and hence it is possible that nuclear plant disasters create greater psychological stress compared to other tragedies, due to fear of an invisible threat, ambiguous information about the event, and/or forced evacuation 12,63,64 .

Indirect health effects related to nuclear power plant disaster

Proliferation of wildlife and effect on local human populations.

While the genetic effects of chronic low level radiation contamination is still under debate, the lack of human pressure in the evacuation area seems to have led to uncontrolled proliferation of large mammals, including erk, deer, and wild boar, such as noted after the Chernobyl nuclear disaster 65 . In Fukushima, the local government has also found that farmers have increasingly trapped wild boar since the nuclear plant disaster in 2011 66 ( Fig. 3 ). In fact, herds of wild boars were observed in abandoned farmlands in many evacuation zones, which is very unusual in any other part of Japan. Drop in commercial trade and domestic consumption of wild boar meat from the entire Fukushima prefecture (due to abnormal radiodensity in wild boar meat and decrease in the number of licensed hunters) have limited population control 66–68 . In Chernobyl, predators such as wolves helped control proliferation of wildlife to some extent, but in Japan no significant predator for wild boar exists, hence, human activity and limited food supply are the only regulatory factors 65 . This lack of population control seems to also affect animal behavior. Wild boars, which typically have a territory of ~2.5 km 2 , have been reported to have a 4–5 time larger range after the nuclear accident in Fukushima, compared with nonevacuation zones and have become diurnal instead of nocturnal 66 . The increase in number of such wildlife and changes in behavior raise the risk of human-wildlife conflicts in these areas.

The wild boar, a fertile animal requiring only 1.5 years for reproductive maturation, can have an average litter of 4–5 piglets. Without human pressure, wild boar can explosively increase their number and break out to neighboring areas 69,70 . Adult boars are strong and fast and can reach speeds up to 40 km/h, leap 1 m high, and lift over 70 kg objects with their sharp tusks 71 . Wild boars rarely attack humans, but injuries and deaths have been reported 72–78 . Attacking boars first charge victims from the back leading to puncture wounds and victims typically have multiple injuries in the posterior aspect of their lower extremities, especially the thigh. Severe anorectal injuries caused by wild boar have also been reported 78 ( Fig. 4 ).

There are also several important patient related factors including age and comorbidities with relation to outcomes with boar attacks. In fact, 64% of agricultural workers are over 65 and 32% are over 75 79 ( Fig. 5 ). Age related physical limitations may make people more vulnerable to wildlife attack and poorer outcomes as seen in geriatric trauma around the world. Furthermore, elderly people are more likely to have comorbidities, which puts them at a higher risk for postinjury complications 80 .

Other health effects from wildlife

Wildlife may serve as a reservoir for pathogens. Wild boars carry many pathogens such as hepatitis E, tuberculosis, leptospirosis, and trichinellosis 81 . Moreover, large mammals are hosts for ticks, which is a reservoir of tick-borne disease like Rickettsia 82 . Movila et al 83 reported an abundance of adult ticks in the evacuation zone of Chernobyl. No direct relation between nuclear plant disaster and tick-borne disease have been reported, but health care workers in surrounding areas should be aware of a potential increase in wildlife related infectious diseases, especially during the resettlement period.

Nuclear plant disasters may result in various immediate and long-term health effects including behavioral and social consequences. Extensive environmental impact also leads to additional unanticipated effects on wildlife making resettlement more complicated. Health care workers should be aware of these potential threats after a nuclear plant accident in the short and long-term.

Ethical approval

Sources of funding.

This work was supported by the US National Institutes of Health/National Cancer Institute grant 5T32CA108456 to V.M., and US National Cancer Institute Cancer Center support grant P30CA016056 to Roswell Park Comprehensive Cancer Center. The funder was not involved in the study design, collection, analysis, interpretation of data, writing of this article or decision to submit it for publication.

Author contribution

I.O. conceptualized, collected and interpreted data, and prepared the manuscript. K.T. provided supervision of the design of the work and interpretation of the data. I.O., A.R., M.D., V.M., S.J., and K.T. provided critical revisions of the manuscript.

Conflicts of interest disclosure

The authors declare that they have no financial conflict of interest with regard to the content of this report.

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Review article, critical review of nuclear power plant carbon emissions.

• 1 State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, China
• 2 China Nuclear Power Engineering Co., Ltd., Beijing, China
• 3 College of Management and Economics, Tianjin University, Tianjin, China
• 4 Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
• 5 National Marine Data and Information Service (NMDIS), Ministry of Natural Resources of China, Tianjin, China
• 6 Beijing Key Laboratory of Wetland Ecological Function and Restoration, Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing, China
• 7 The Center for Eco-Environmental Accounting, Chinese Academy of Environmental Planning, Beijing, China
• 8 China Academy of Urban Planning and Design, Beijing, China

Nuclear power plays a crucial role in achieving the target of carbon neutrality to build a sustainable society. However, it is not “carbon-free” when considering its entire life cycle. Therefore, accurate accounting and monitoring of its generated carbon emissions are required to avoid miscalculations of nuclear energy as a clean energy source. In this study, the life-cycle carbon emissions of nuclear power plants (NPPs) with different reactor types are reviewed. In addition to the characteristic differences among different reactors, disparities in the review results originate from the varying emissions at the respective stages of the nuclear fuel cycle, technology choices at each stage and accounting methods and boundaries. The carbon emissions resulting from NPP construction and operation are underestimated due to the limited data and methods, which creates uncertainty in the evaluation of NPP carbon emissions. An integrated framework for carbon emissions accounting considering the construction and operation of NPPs (CACO-NPP) is proposed. This integrated framework aims to improve the accounting accuracy for carbon emissions originating from NPPs. An emerging Generation III NPP with the latest technology, HPR1000 (an advanced pressurized water reactor), was adopted as a case study. The results show that the total emissions resulting from vegetation loss, equipment manufacturing and labor input during construction and operation are 1232.91 Gg CO 2 with a carbon intensity of 1.31 g CO 2 /kWh, indicating the notable mitigation capability of Generation III NPPs. By combining the maturity of HPR1000 technology with successive design improvements, the carbon emissions of such reactor types could be further reduced. This development is very important for realizing China’s carbon neutrality target.

1 Introduction

China has pledged to realize CO 2 emission peaking by 2030 and achieve carbon neutrality by 2060, which is referred to as the carbon peaking and neutrality targets ( Yan et al., 2022 ). The required speed and scale of China’s emission reduction efforts are unique relative to other countries because the time is limited for China to reach the above targets ( Guan et al., 2021 ). Since the power sector is the major source of carbon emissions during electricity generation and consumption, clean energy is an ideal choice to curb emissions ( Li et al., 2018 ; Naimoğlu, 2022 ). Reportedly, to achieve the 1.5°C target, clean energy should account for 50% of the total energy sources by 2050 ( IEA, 2019 ).

Nuclear energy supplies high base-load electricity, which is more reliable, sustainable and economic than other clean energy sources, such as hydropower, solar power and wind power ( Rawat et al., 2017 ). Moreover, pursuing nuclear energy has become prominent to reach the Sustainable Development Goals (SDGs) including SDG 13 (climate action), SDG 7 (affordable, reliable and modern energy sources), SDG 12 (sustainable consumption and production modes) and SDG 11 (sustainable cities and communities) ( Acheampong et al., 2017 ; Gunnarsdottir et al., 2021 ; Wang et al., 2023a ). As a result of the SDG agenda adopted in 2015, countries are more eager than ever to deploy nuclear power aiming to pursue a sustainable environment and economic growth ( Wang et al., 2023a ).

Nuclear energy contributes to energy security and hence secures economic progress and social wellbeing in populous countries ( Wu et al., 2022a ). At the end of 2020, the world’s total nuclear power capacity reached 392.6 GW(e), generated by 442 operational nuclear power reactors in 32 countries, accounting for 10% of the world’s total electricity generation ( IAEA, 2021 ). China has been committed to developing nuclear power in an active, safe and orderly way ( Xi, 2022 ), witnessing a steady increase in electricity generation by nuclear power since 2010 ( Gungor and Sari, 2022 ). China’s nuclear power generation in 2021 amounted to 407.14 billion kWh, increasing 11% over 2020 levels ( Zhang et al., 2021 ). Generation III nuclear power plants (NPPs), represented by the latest technology (HPR1000), could have a promising future development potential, with 6–8 units constructed annually ( Zhang et al., 2021 ).

Although no carbon is directly generated or released in the fission reaction due to the lack of fossil fuel consumption during nuclear electricity production, indirect carbon emissions are generated from the extraction and conversion of raw materials, construction of power plants and other process steps in the entire nuclear fuel cycle ( Beerten et al., 2009 ). The nuclear fuel cycle is generally classified into two types: the once-through cycle (OTC) and the twice-through cycle (TTC). Spent fuels are directly discharged in the former mode, while unused fissionable materials are recycled through reprocessing in the latter mode. Both the OTC and TTC modes involve complex stages, including the front end (uranium mining and milling, conversion, enrichment and fuel fabrication), construction and operation of NPPs, back end (interim storage, conditioning, and reprocessing) and a final stage involving plant decommissioning ( Atz and Fratoni, 2023 ). Varying estimates have focused on the carbon emissions of the nuclear fuel cycle (expressed in g CO 2 /kWh) for a specific reactor type or fuel cycle mode. However, few studies have compared carbon emissions originating from different reactor types and fuel cycle modes. Moreover, the critical stages and technological factors influencing carbon emissions from NPPs must be determined. Since carbon accounting is relevant in terms of reaching the Chinese carbon peaking and neutrality targets and is also the foundation of establishing a cap-and-trade system for carbon emissions ( Xi, 2022 ), it is important to clarify the carbon emissions originating from different nuclear reactor types and formulate suggestions to reduce emissions of NPPs.

In this study, we aimed to provide a comprehensive and critical review of carbon emissions originating from NPPs with different reactor types, including pressurized water reactors (PWRs), light water reactors (LWRs), heavy water reactors (HWRs), boiling water reactors (BWRs), fusion reactors (FRs), fast breeding reactors (FBRs) and gas-cooled reactors (GCRs). The disparity of the review results was analyzed in terms of the various stages involved in the nuclear fuel cycle and process selection at each stage. Moreover, method-related factors, including accounting methods and accounting boundaries, were investigated to clarify the source of discrepancy in the review results. Furthermore, viable pathways were proposed to curb these carbon emissions within the context of the whole nuclear fuel cycle. Based on the abovementioned review and analysis, we established an integrated methodological framework to improve the accuracy of NPP emissions accounting. Based on the proposed framework, we analyzed the mitigation capability of a Generation III NPP using the latest technology (HPR1000) in China and elucidated the techno-economic aspects of this nuclear reactor type to provide a greater understanding. Finally, conclusions and implications were outlined, aiming to improve the sustainable development of the nuclear power industry toward achieving carbon neutrality.

2 Review of the carbon accounting results for NPPs based on different nuclear reactors

Traditionally, estimates of carbon emissions of NPPs are based on the nuclear fuel cycle, focusing on the carbon emissions resulting from respective stages including mining, milling, refinery, conversion, enrichment and fuel fabrication in the front end, construction and operation of NPPs as well as interim storage, reprocessing, mixed oxide (MOX) fuel fabrication and final disposal in the back end. Moreover, the transportation of spent fuels and radioactive wastes is included. Table 1 provides an overview of the reviewed studies on carbon emissions based on the nuclear fuel cycle. The carbon emission results are reported as g CO 2 /kWh. Different studies show a wide disparity due to the diverse reactor types, various technological processes at each stage of the nuclear fuel cycle, applied accounting methods and considered accounting boundaries ( Pomponi and Hart, 2021 ).

TABLE 1 . Reviewed carbon emissions of NPPs.

Figure 1 shows the carbon emissions in the respective studies for different nuclear reactor types. The number of case studies for LWRs, PWRs, BWRs, HWRs, FRs, FBRs, and GCRs are 10, 15, 6, 2, 4, 2, and 1, respectively ( Table 1 ). The carbon emissions of LWRs, PWRs, BWRs, HWRs, FRs, FBRs and GCRs reach 6∼60, 2∼337.42, 11∼24.2, 3.2∼66, 9∼46.52, 2.33∼6.26 and 8.35 gCO 2 /kWh, respectively. Notably, the disparity of the results is larger for PWRs than for the other reactor types because the number of PWR-related studies is the largest with varying NPP locations and operational periods, as well as distinct accounting methods and research boundaries. The average carbon emissions of LWRs, PWRs, BWRs, HWRs, FRs and FBRs are 17.88, 42.94, 19.68, 37.40, 25.05, and 4.30 g CO 2 /kWh, respectively. The average emissions of PWRs are higher than those of HWRs because HWRs can be fueled with natural uranium or slightly enriched uranium (below 2%) ( Wu et al., 2022b ). Compared to PWRs, HWRs provide the advantage of not requiring an enrichment process for fuel fabrication, resulting in reduced energy consumption and carbon emissions. Compared to those of the other reactor types, FBRs produce the lowest carbon emissions on average because the uranium resource consumption and corresponding energy use in the front end and the volume of high-level radioactive waste are reduced due to the higher levels of uranium and plutonium recycling ( Bodi et al., 2022 ). This result is consistent with the studies of Kadiyala et al. (2016) and Poinssot and Bourg (2021) . The median carbon intensity of FRs is the highest due to its complex design and construction based on current conditions. Because the number of studies on GCR emissions is small, the objective results for these reactors and a comparison to other types of reactors should be further investigated in the future.

FIGURE 1 . Quartile boxes figure of carbon emissions originating from the different types of nuclear reactors (PWR, pressurized water reactor; BWR, boiling water reactor; FR, fusion reactor; FBR, fast breeding reactor; GCR, gas cooled reactor).

3 Review of the carbon accounting results for NPPs based on the different stages in the nuclear fuel cycle

To clarify the disparity among the reviewed studies, it is necessary to expand our focus and consider the whole nuclear fuel cycle, involving the front end, construction and operation of NPPs and back end. The considerable variability of carbon emissions at the respective stages is due to the differences in energy and material inputs. Moreover, the technology choices at each stage of the nuclear fuel cycle influence the carbon emissions of NPPs. Therefore, studying the carbon emissions at each stage of the nuclear fuel cycle and identifying the main factors influencing these emissions could be conducive to a comprehensive understanding of the disparity mentioned in Section 2 .

3.1 Carbon emissions at the front end

The carbon emissions of the nuclear fuel cycle are related to the technology applied at each stage of the front-end nuclear fuel cycle. The carbon emissions produced in the front end, especially uranium mining, milling and enrichment, dominate the whole nuclear fuel cycle ( Fthenakis and Kim, 2007 ; Sovacool, 2008 ).

3.1.1 Mining and milling

The carbon emissions resulting from mining and milling vary mainly due to the differences in mine type and ore grade. Uranium is mined via either open pit, underground excavation or in situ leaching methods. Compared to those of the latter two processes, open pits release higher carbon emissions due to the higher energy and material inputs ( Lenzen, 2008 ; Sovacool, 2008 ). In contrast, in situ leaching involves the lowest energy and material consumption as milling is avoided. However, carbon emissions could also be generated due to the use of acid and lime in the process of uranium leaching and neutralization of leached tailings ( Krūmiņš and Kļaviņš, 2023 ).

Ore grade is another factor affecting carbon emissions resulting from mining and milling. Emissions increase with decreasing uranium ore quality due to the higher energy and material consumption levels in mining and milling. When the uranium ore grade declines by a factor of ten, the energy inputs to mining and milling increase by at least a factor of ten ( Mark and Peter, 2006 ). Parker et al. (2016) estimated carbon emissions resulting from uranium mining and milling in Canada, reporting that emissions increased from 0.9 to 2.2 g CO 2 e/kWh when the ore grade declined from 4.53% to 0.74%. Compared to other estimates, the highest carbon emissions resulting from mining and milling (24.73 g CO 2 e/kWh) were reported by van Leeuwen and Smith (2007) , where the uranium ore grade reached only 0.06%, while the lowest emissions (0.1 g CO 2 e/kWh) were reported by Fthenakis and Kim (2007) , where the uranium ore grade was 12.7%.

3.1.2 Enrichment

Enrichment is an indispensable stage in the front end of the nuclear fuel cycle involving uranium enrichment from the natural concentration to approximately 3% (typical for LWRs). Generally, the adopted enrichment methods mainly include gas diffusion and gas centrifugation. Gas diffusion is a much more energy-intensive technique and emits more carbon than gas centrifugation ( Dones et al., 2005 ). Lenzen (2008) reviewed the energy requirements (in units of kWh/kg separative work unit (SWU)) for gas diffusion and gas centrifugation. The energy input of gas diffusion (2,400∼3,100 kWh/kg SWU) was significantly higher than that of gas centrifugation (40∼282 kWh/kg SWU) ( Lenzen, 2008 ). Correspondingly, the carbon emissions resulting from gas diffusion could also be significantly higher than those resulting from gas centrifugation ( Lenzen, 2008 ). The emissions resulting from gas diffusion (80 g CO 2 e/kWh) were approximately 10 times higher than those resulting from gas centrifugation (9 g CO 2 e/kWh), in which the electricity supplied for the former process is obtained from fossil fuels, while the electricity supplied for the latter process is obtained from renewable energy sources ( Dones et al., 2004a ). Moreover, the carbon emissions resulting from enrichment are prominent throughout the whole nuclear fuel cycle, accounting for 61.9% and 55.8% of the total emissions of the nuclear fuel cycle in the OTC and TTC strategies, respectively. The amount of carbon emissions resulting from enrichment is directly related to the source of the electricity supply ( Hondo, 2005 ). Similarly, Lenzen (2008) reported that the emissions resulting from enrichment dominate the nuclear fuel cycle, accounting for approximately 37%∼65% of the total emissions. The contribution varied due to the different sources of the electricity supply. Adopting gas centrifugation and using renewable sources for electricity generation are effective measures to curb the carbon emissions resulting from enrichment and thus reduce the emissions of NPPs.

3.2 Carbon emissions resulting from the NPP construction and operation

The carbon emissions generated during the construction of NPPs result from the use of bulk materials and fossil fuels. Estimates of the carbon emissions resulting from construction and operation vary widely due to the methods adopted (IO or process chain analysis (PCA)), studied reactor type, input data and estimates and assumptions ( Lenzen, 2008 ; Beerten et al., 2009 ). Sovacool et al. (2008) demonstrated that, compared to front-end emissions, the emissions originating from the construction phase are lower, accounting for approximately 12% of the total emissions of the nuclear fuel cycle. However, Poinssot et al. (2014) published contradictory findings, revealing that the contribution of the emissions resulting from construction is approximately the same as that of the front-end emissions. Similar results were also reported by van Leeuwen and Smith (2007) and Jiang et al. (2015) . Compared to those of the commonly adopted PWRs and BWRs, the emissions resulting from the construction of advanced reactors, namely, FBRs and HWRs, are higher due to their more complex design and additional components ( Bodi et al., 2022 ).

The carbon emissions stemming from the construction phase are currently underestimated. Studies on s NPP construction emissions are mainly based on approximate accounting of the emissions resulting from the consumption of steel, concrete, copper and cement ( Ma et al., 2001 ; Fthenakis and Kim, 2007 ). However, detailed information on the input data is lacking, and accurate emissions accounting cannot be realized solely based on rough material consumption estimates ( Beerten et al., 2009 ). Moreover, the construction of NPPs, especially nuclear islands, involves equipment with a large gross mass using advanced stainless steel for safety reasons. For instance, the total weights of the reactor pressured vessel and steam generator are approximately 418 and 365 tons, respectively, using 16MnD5 (the designation of carbon steel plates used specifically in nuclear power construction) as the main material, which is carbon intensive ( Xing and Wu, 2020 ; Ju et al., 2022 ). The omission of carbon emissions resulting from equipment manufacturing could result in the underestimation of carbon emissions stemming from NPP construction. In addition to equipment manufacturing, there are other factors influencing carbon emissions during the construction phase that should be further investigated.

Similar to the construction stage, the carbon emissions resulting from the operation of NPPs vary widely. The reported emissions at this stage are low, accounting for approximately 6%∼17% of the total carbon emissions of the nuclear fuel cycle ( Lenzen et al., 2006 ; Sovacool, 2008 ; Pomponi and Hart, 2021 ). In current studies, the emissions at this stage are underestimated because the emissions resulting from the manufacturing of equipment and replacement nuclear fuels and fossil fuel consumption for material transportation are generally omitted. Moreover, detailed input data for carbon accounting, such as the consumption of chemicals, electricity and diesel used during operation and maintenance, are difficult to obtain and analyze ( Pomponi and Hart, 2021 ). Therefore, a systematic approach for accurate accounting of the emissions at the operation stage is needed.

3.3 Carbon emissions at the back end

The carbon emissions at the back end mainly result from the final disposal (in the OTC case) and reprocessing of spent fuels (in the TTC case). Sovacool (2008) estimated that the emissions at the back end only accounted for approximately 13% of the total carbon emissions of the nuclear fuel cycle. Similarly, Hondo (2005) reported that the OTC- and TTC-based emissions at the back end accounted for 3.4% and 6% of the total emissions of the nuclear fuel cycle, respectively. Poinssot et al. (2014) also found that back-end emissions only accounted for 7% or even less of the total cycle emissions. Although the reported emissions seem low relative to the total emissions of the nuclear fuel cycle, the results exhibit high uncertainty. The input data for back-end facilities are very limited because projects involving final repositories and reprocessing are generally confidential, and relevant input data are difficult to acquire. Therefore, back-end emissions are difficult to accurately estimate, and the present results may exhibit high uncertainty.

4 Review of the accounting methods for NPP carbon emissions

4.1 development of methods.

Life-cycle assessment (LCA) is a widely utilized methodology to determine the environmental impacts of a given product or system on a life-cycle basis ( Nitschelm et al., 2021 ). LCA has been used to evaluate the life-cycle carbon emissions of NPPs ( Anshassi et al., 2021 ; Pomponi and Hart, 2021 ). The accounting methods for carbon emissions of the nuclear fuel cycle broadly involve bottom-up and top-down approaches ( Pomponi and Hart, 2021 ).

PCA is a bottom-up approach that enables carbon accounting at the process level with high granularity ( Nian et al., 2014 ). In PCA, process steps are systematically itemized and decomposed into subprocesses, where input factors relevant to carbon emissions are itemized, such as materials and energy ( Ehmsen et al., 2021 ). PCA provides high granularity in accounting for carbon emissions resulting from each stage of the nuclear fuel cycle because it considers the materials and energy consumption of each engineering process ( Liu et al., 2019 ). This method could provide useful insights given specific research objectives (products and processes) and when detailed inventory data are available ( Pomponi et al., 2022 ). However, it is often difficult to acquire extensive and highly accurate data. Moreover, due to inconsistent and incomplete research boundaries, the PCA method fails to capture carbon emissions associated with auxiliary processes, services and supply chains ( Xiong et al., 2021 ; Yu et al., 2021 ). For instance, some processes cannot be expressed as materials or energy used, and thus, corresponding emissions are impossible to determine via this approach ( Voorspools et al., 2000 ). For instance, in large construction projects, it is very difficult to obtain detailed process-based carbon emission factors for equipment manufacturing with only monetary data ( Li et al., 2021 ).

The top-down approach mainly includes the input-output (IO) method, which is a robust method for evaluating energy systems at the macroeconomic level by formulating IO tables. Embedded carbon emissions in end-products or services can be determined based on the average carbon emission intensity of each economic sector ( Zhang et al., 2022 ). Regional IO and multiregional input-output (MRIO) models are two approaches to the IO method ( Ali et al., 2018 ). The IO approach considers sectoral interactions and facilitates the quantification of complex linkages, providing a useful tool to clarify the embedded emissions in the whole production chain ( Zhang et al., 2022 ). Generally, the IO method can be employed to effectively estimate and analyze direct and indirect sectoral carbon emissions ( Li et al., 2023 ; Liu et al., 2023 ). However, some stages cannot be applied to all economic sectors, and thus, their emissions cannot be evaluated via the IO method. In addition, this approach imposes a time lag effect on carbon emission analysis ( Zhou et al., 2023 ), lacks granularity at the engineering process level and fails to provide specific results for products and processes ( Xiong et al., 2021 ).

White and Kulcinski (2000) compared the IO and PCA methods, demonstrating that the former approach could exhibit overestimation of the results because costs not directly relevant to carbon emissions originating from materials are involved as input. Moreover, the PCA method probably underestimates carbon emissions due to the neglect of embodied emissions in the supply chain. Previous work has shown that carbon emissions accounted for by the IO method are higher than those accounted for by the PCA method ( Pomponi and Hart, 2021 ; Pomponi et al., 2022 ).

Considering the advantages and drawbacks of each method, a hybrid LCA approach combining both the IO and PCA methods has been widely applied, denoted as a hybrid LCA (HLCA) model ( Xiong et al., 2021 ). The HLCA model provides a holistic perspective for carbon emissions accounting, and it has been reported that this hybrid method can yield more accurate results ( Pomponi and Lenzen, 2018 ).

The materials used in the nuclear industry are normally expensive, labor intensive and energy intensive. The material cost generally integrates the costs of instrumentation- and control-related energy, extra energy for manufacturing and specification and labor use. These extra consumption aspects and corresponding indirect emissions are not involved in PCA ( Fthenakis and Kim, 2007 ; Tang et al., 2022 ). Thus, the HLCA method has been applied in a series of studies to account for the carbon emissions of NPPs, combining the use of the IO and PCA methods ( White, 2000 ; Lenzen et al., 2006 ; van Leeuwen and Smith, 2007 ; Lenzen, 2008 ; Warner and Heath, 2012 ; Pomponi and Hart, 2021 ). When possible, the PCA method can be employed, while the IO method can be adopted for carbon accounting of nonmaterial processes and complex equipment.

In addition to the LCA methodology, the average energy intensity (AEI) method has been applied to calculate the carbon emissions resulting from NPP construction ( Pomponi and Hart, 2021 ). In the AEI method, the total cost is multiplied by the national AEI value. This method is easy to use but can suffer overestimation, with approximately 10 and 5 times higher emissions than those obtained using the PCA and IO methods, respectively ( Lenzen et al., 2008 ).

4.2 Research boundary

In 21 of the 24 studies reviewed in this paper, the carbon emissions of NPPs were estimated based on the whole nuclear fuel cycle ( Table 2 ), involving the front end, construction and operation of NPPs and back end. Voorspools et al. (2000) estimated carbon emissions resulting from the construction, maintenance and demolition of PWR-type NPPs. The estimated emissions were 2 and 4 g CO 2 /kWh based on the PCA and IOA methods, respectively, which are on the lower end of the range of estimates for PWRs (2∼337.42 g CO 2 /kWh). This finding can be explained by the narrower accounting boundary relative to other studies. Similarly, Pomponi (2021) investigated carbon emissions resulting from the construction and operation of a European pressurized reactor (EPR) based on the PCA and IOA methods. However, the results obtained with the PCA (16.55∼17.69 g CO 2 /kWh) and IOA (18.82∼35.15 g CO 2 /kWh) methods were higher than previous results. The reactor studied by Voorspools et al. (2000) is a Generation II PWR, while the EPR is a Generation III PWR, which is a more energy- and material-intensive reactor type during its construction and operation phase and more expensive than the former reactor type ( Pomponi and Hart, 2021 ). Parker et al. (2016) comprehensively assessed the carbon emissions resulting from the mining and milling of uranium, aiming to improve the data accuracy for the accounting of carbon emissions of NPPs. Other studies covered the whole nuclear fuel cycle. All the phases of the front end of the cycle were considered in these studies, but the stages of the back end were considered in different ways among the various studies. The reason lies in the type of nuclear fuel cycle (OTC or TTC) studied.

TABLE 2 . Research boundaries of the different studies.

Since PWRs are the most common nuclear power reactors, the breakdown of carbon emissions among the front end, construction and operation of NPPs and back end of the nuclear fuel cycle was analyzed ( Figure 2 ; Supplementary Materials ). The average carbon emissions at the front end, construction and operation and the back end were 11.45, 7.82 and 3.07 g CO 2 /kWh, respectively. The front-end emissions comprised the largest component, accounting for approximately half of the total emissions of the nuclear fuel cycle, followed by the emissions resulting from the construction and operation of NPPs and the back end. The carbon emissions at the front end were mainly generated from mining and milling as well as enrichment. However, different studies have reported contradictory results regarding the composition of front-end emissions. Poinssot et al. (2014) , Jiang et al. (2015) and Serp et al. (2017) revealed that emissions resulting from mining and milling were the most prominent component, accounting for approximately 62%–78% of the total front-end emissions. However, Ma et al. (2001) , Hondo (2005) , Fthenakis and Kim (2007) and Nian et al. (2014) demonstrated the opposite findings whereby the enrichment emissions constituted the largest part, accounting for approximately 75%∼88% of the total front-end emissions. Such conflicting results can be attributed to the different technology choices and sources of consumed energy at each stage of the front end within the respective studies ( Section 3.1 ).

FIGURE 2 . Breakdown of the carbon emissions of the nuclear fuel cycle of PWRs.

5 Pathway to curb carbon emissions based on the nuclear fuel cycle

The nuclear fuel cycle involves both the OTC and TTC strategies. In the OTC mode, spent nuclear fuels are regarded as highly radioactive waste materials to be finally disposed, which is also called the open cycle ( Figure 3 ). In contrast, in the TTC mode, spent nuclear fuels are reprocessed to recycle U and Pu and fabricate MOX fuels, also called the closed cycle ( Yang, 2016 ) ( Figure 4 ). Compared to the OTC mode, the TTC mode is more environmentally friendly with less disturbance to the ecological environment. Among the 24 studies, 10 considered the TTC strategy, and thus, the carbon emissions stemming from reprocessing were involved in the fuel cycle ( Table 2 ). The carbon emissions in the OTC mode (5.45 g CO 2 /kWh) were higher than those in the TTC mode (5.29 g CO 2 /kWh). This agrees with the study of Poinssot and Bourg (2021) , who estimated that the carbon emissions resulting from the TTC strategy were approximately 3% lower than those resulting from the OTC strategy. When shifting from the TTC to OTC mode, the carbon emissions resulting from mining, conversion and enrichment could significantly increase due to the increased natural uranium consumption as well as energy consumption in the front end ( Poinssot et al., 2014 ; Poinssot and Bourg, 2021 ). Although the TTC mode involves reprocessing of nuclear spent fuels, the contribution of emissions resulting from reprocessing to the whole cycle emissions is low (lower than 7%) ( Poinssot et al., 2014 ). Moreover, the carbon emissions resulting from the disposal of the large volume of high-level radioactive waste produced in the OTC strategy could be two times higher than those produced in the TTC strategy ( Poinssot et al., 2014 ).

FIGURE 3 . Schematic diagram of the once-through cycle (OTC).

FIGURE 4 . Schematic diagram of the twice-through cycle (TTC).

Under the same discount rate, there was no apparent discrepancy in the unit cost of the back end between the OTC and TTC strategies, even considering the external cost ( The Boston Consulting Group for AREVA, 2006 ; Tang, 2021 ; Kim et al., 2022 ). Based on the whole nuclear fuel cycle, Taylor et al. (2022) verified the approximate economics of the OTC and TTC strategies regarding the levelized cost of electricity (LCOE) attributed to the minor component of back-end costs. Moreover, compared to the TTC mode, the higher volume of high-level radioactive waste in the OTC mode necessitates the construction and operation of a deep geologic repository, resulting in higher carbon emissions and financial input over a long timescale ( Taylor, 2022 ).

Therefore, the TTC strategy is advantageous in terms of the efficient use of natural resources, reduction in the waste volume and radiotoxicity and carbon mitigation without exerting unfavorable impacts on the total cost of nuclear power generation. In particular, with the expansion of China’s nuclear power, the external dependence on uranium resources has increased, posing great risks to nuclear power development ( Guo et al., 2023 ). Within the context of advocating the circular economy in China, implementing the TTC strategy could be a viable pathway to enhance the mitigation capability of NPPs.

6 Discussion

6.1 modified framework for carbon emissions accounting for the construction and operation of npps (caco-npp).

In the nuclear fuel cycle, front-end emissions have been extensively studied relative to the back end and the construction and operation phase. Moreover, back-end emissions account for only 14% of the total emissions of the nuclear fuel cycle, and the input data are difficult to acquire due to the confidentiality of reprocessing projects. As mentioned in Section 3 , the estimated carbon emissions resulting from construction and operation are highly contradictory among the different studies, accounting for an average of 35% of the total carbon emissions of the nuclear fuel cycle, and these emissions are currently underestimated due to the limited input data and incomplete methodologies. Such uncertainty in the carbon emissions stemming from construction and operation could hinder the objective and comprehensive evaluation of the carbon emissions of the nuclear fuel cycle, impeding the accurate estimation of the emission mitigation capability of NPPs. Since the construction sector contributes almost 30% to the global carbon emissions, it plays a significant role in achieving SDGs and carbon neutrality ( Alawneh et al., 2018 ; Opoku et al., 2022 ).

Developing an appropriate model to comprehensively and accurately identify emissions resulting from NPP construction and operation is urgent. The Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines for National Greenhouse Gas Inventories and the updated 2019 document proposed three tiers for carbon emission estimation ( IPCC, 2006 ; IPCC, 2019 ). Tier 3 is the highest level of carbon accounting with more complex models and higher resolution input data, and it can better capture variability in local conditions ( Sperow, 2020 ). This is especially necessary for China, where HPR1000, a Generation III nuclear power reactor, will be the dominant reactor type in the future, and the emissions of such emerging reactors are still unclear due to the new design concepts and system components.

Constructed NPPs and their surrounding environments could be regarded as social-economic-natural complex ecosystems ( Wang and Ouyang, 2012 ). Emissions accounting should also be comprehensive, not only involving energy and material consumption, as was considered in previous studies, but also involving the influence of NPP construction and operation on the ecological system as well as emissions resulting from human activities. Figure 5 shows a schematic framework of the proposed CACO-NPP methodology, by which emissions can be determined within the context of social-economic-natural complex ecosystems.

FIGURE 5 . Integrated framework for carbon emissions accounting for the construction and operation of NPPs (CACO-NPP) PCA, Process chain analysis; IO, Input‒output analysis; RS, Remote sensing.

Human activities are the most active element in the social-economic-natural complex ecosystem and dominate the NPP construction and operation stage ( Ma and Wang, 1984 ). Moreover, the construction and operation of NPPs could influence the natural ecosystem by altering the land use type from vegetative land to industrial land. Such transformation could result in the loss of on-site vegetation and biomass carbon. Equipment is of paramount importance to NPP operation. Numerous equipment types occur at NPPs distributed across nuclear island, balance of plant and conventional island. Equipment manufacturing exhibits the characteristics of a strong foundation and high industrial linkages, which provides important support to promote industrial and economic development. Equipment manufacturing not only consumes many raw materials and much energy but also promotes the development of downstream service industries. It has the industrial characteristics of connecting the preceding and the following. Equipment manufacturing depends on input obtained from other national economic sectors.

Estimation of the carbon emissions resulting from vegetation loss, human activities and equipment manufacturing is essential to quantify the impact of NPPs on the social-economic-natural complex ecosystem as well as to bridge the gap in regard to the current underestimation of carbon emissions resulting from NPP construction and operation. In this study, relevant data for Zhangzhou NPP Units 1 and 2, an emerging Generation III NPP with the latest technology (HPR1000), were acquired to quantitatively estimate emissions originating from the abovementioned sources.

6.2 Carbon emissions from vegetation deterioration

The construction of NPPs could cause vegetation deterioration in the ecosystem, resulting in corresponding biomass carbon loss. NPPs in China generally occur in the coastal area of southeastern China, where the forest carbon density is the highest (2.88 t C/hm 2 ) ( Liu Z. et al., 2022 ). An NPP, normally comprising 6 units, covers approximately 200 ha of land, indicating that NPP construction could lead to the immediate loss of 2,112 t CO 2 ( Peng, 2021 ). Cui et al. (2016) used remote sensing data to model the carbon sequestration rate of vegetation from 2001 to 2010 and reported that the carbon sequestration rate in southeastern China reached over 600 g/(m 2 ·a). Thus, the vegetation lost due to NPP construction could have sequestered 78,000 t C given the life period of 65 years for both the construction and operation of Generation III NPPs.

Remote sensing is a feasible vegetation monitoring means, which is vital for the sustainability of NPPs ( Lu et al., 2018 ). NPP construction and operation could result in vegetation carbon loss at the construction site and in the proximity of the plant. Such carbon loss should be evaluated using remote sensing techniques ( Figure 5 ). At the construction stage, remote sensing contributes to the siting and site evaluation process, aiming to identify the land use type and avoid unacceptable environmental impacts ( Baskurt and Aydin, 2018 ). During the operation period, medium-resolution satellite remote sensing sources could be used via continuous monitoring to compare the vegetation biomass and biomass carbon sink around the plant to past conditions at the same location ( Chen et al., 2019 ). The application of remote sensing as a supplement to the methodology of carbon accounting for NPPs is essential to comprehensively evaluate the effects of NPPs on the regional carbon budget.

According to the land use data for Zhangzhou NPP Units 1 and 2 retrieved from the preliminary project research report and the carbon stock of respective ecosystems, including forestlands, grasslands, croplands, gardens and wetlands in Fujian Province 2015, the carbon emissions due to vegetation loss resulting from NPP construction were estimated ( Table 3 ) ( National Development and Reform Commission NDRC and National Bureau of Statistics of China NBSC, 2022 ). The direct carbon loss results from vegetation deterioration during preliminary NPP construction, while the potential carbon loss is the carbon sequestration potential of the destroyed vegetation during the operation period of NPPs (60 years). The total carbon emissions resulting from vegetation loss during NPP construction and operation reached 842.6 t CO 2 , 84.91% of which was the potential carbon loss during the operation period.

TABLE 3 . Vegetation carbon loss due to NPP construction.

6.3 Embodied carbon emissions from equipment manufacturing

In this section, we investigate the embodied carbon emissions in equipment manufacturing based on a whole life-cycle perspective and quantitatively assess the embodied carbon emissions by constructing a top-down IO-LCA model. This method can achieve comprehensive assessment of carbon emissions accounting. Previous studies of carbon emissions of NPPs did not involve emissions resulting from equipment manufacturing, which should also be considered. Adopting China’s Zhangzhou NPP Units 1 and 2 as an example, the number of process equipment and valves in the nuclear island reaches over 1,600 and 8,500, respectively. Moreover, much utility equipment is required for instrumentation and control (I&C), ventilation, power supply, communication and fire production. It is difficult to count the detailed energy and material consumption levels for each equipment manufacturing process. Therefore, it is unreasonable to use the PCA method in this case. As mentioned in Section 4 , the IO method can be adopted to estimate the carbon emissions resulting from equipment manufacturing. The emissions resulting from equipment manufacturing can be calculated via the equipment procurement expenses and the carbon emission coefficient values of the corresponding sectors ( Liu et al., 2020 ; Li et al., 2021 ).

The IO-LCA model requires data derived from national or regional IO tables, which greatly reduces the difficulty of data acquisition over the bottom-up life-cycle method (PCA) ( Wang et al., 2022 ). To ensure consistency with China’s 42-sector classification of the national IO table in 2017 ( National Bureau of Statistics of China, 2018 ; National Bureau of Statistics of China, 2020 ), we used the 42-sector IO table for Fujian Province in the calculation process. According to the 2017 IO table of Fujian Province, the equipment used in NPPs can be categorized into 5 corresponding sectors, namely, the manufacture of ordinary machinery, equipment for special purposes, electric equipment and machinery, electronic and telecommunications equipment, and instruments and meters. Detailed formulas of the model are presented in the Supplementary Materials .

Figure 6 shows our estimated embodied carbon emissions of equipment manufacturing in the respective sectors based on the nuclear island (NI), balance of plant (BOP), conventional island (CI) and preliminary engineering (PE) categories. The total embodied carbon emissions resulting from equipment manufacturing of Zhangzhou NPP Units 1 and 2 reached 1,048.18 Gg CO 2 of which the NI, BOP, CI and PE emissions accounted for 59.26%, 13.09%, 27.13% and 0.53%, respectively. NI is the core part of NPPs and embodies important systems, including nuclear reactors, reactor cooling systems, reactor fuel storage systems, specialized safety systems and nuclear auxiliary systems ( Zhou, 2012 ). Further focusing on the NI emission mix, the emissions of equipment for special purposes accounted for 72.16% of the total NI emissions ( Figure 6 ). The equipment for special use in the NI category normally involves the use of specialized steel (for instance, the material of the reactor pressure vessel is 16MND5) with high safety standards, leading to energy-intensive properties ( Zhu et al., 2022 ). The manufacturing of such special equipment in NI involves higher energy, material and labor inputs, and thus, the emission intensity of equipment for special purposes is approximately 70.1 kg CO 2 /10 3 RMB.

FIGURE 6 . Embodied carbon emissions of equipment manufacturing in each sector (Gg CO 2 ).

Regarding the whole NPP, the emissions of equipment for special purposes and ordinary machinery constitute the two largest parts, accounting for 49% and 31%, respectively, of the total emissions ( Figure 7 ), followed by the emissions originating from electric equipment and machinery, I & C and electronic and telecommunications equipment.

FIGURE 7 . Emission composition of equipment manufacturing.

6.4 Embodied carbon emissions from labor

During NPP construction and operation, a portion of the resources and materials is consumed to sustain workers and staff. Thus, the emissions resulting from labor should also be considered. The annual carbon emissions of China’s labor use are 3.63 tons CO 2 /capita ( Liu et al., 2020 ). More specifically, Qu et al. (2018) reported annual carbon emissions in different regions of China. The yearly emissions of labor use in southeastern China, where most Chinese NPPs are located, reached 3.2 tons CO 2 /capita.

Labor use at NPPs mainly includes four parts: personnel of the owner of the NPP for the preparation of operations and startups, staff of general contractors supplying support for NPP construction, workers of the construction unit and staff of the owner of the NPP for its operation. According to the construction experience of Zhangzhou NPP Units 1 and 2, the total labor inputs of the NPP owner for the preparation of operations and startups were 1,941 and 522 individuals, respectively ( Xu, 2020a ; 2020b ). The number of personnel contributed by the general contractor during NPP construction was 1,094 individuals ( Xu, 2020c ). Construction of NPPs is labor intensive, and the total labor input for civil engineering and installation is 14,000 individuals ( Wang, 2016 ; Xu, 2020c ). The number of staff members committing to operation is 800 individuals ( Xing, 2020 ). Figure 8 shows the embodied carbon emissions of the labor input at the different stages for NPP construction and operation. The total personnel emissions reached 183,882 t CO 2 and the emissions during the operation period were prominent, accounting for 83.53% of the total labor emissions due to the continuous labor input during 60 years of operation. The personnel emissions resulting from the other phases were minor relative to the operation phase.

FIGURE 8 . Embodied labor emissions at the respective stages for the construction and operation of NPPs.

6.5 Mitigation capability and economic competitiveness of HPR1000

The life-cycle power generation of Zhangzhou NPP Units 1 and 2 is 947 × 10 3  GWh, and the emission intensity of the abovementioned emissions during construction and operation is 1.31 g CO 2 /kWh. Compared to the reviewed studies of PWR emissions resulting from construction and operation, our result is lower, indicating the notable mitigation capability of China’s Generation III NPPs.

The unit capital cost of Zhangzhou NPP Units 1 and 2 ranges from 16,000 to 17,000 RMB/kW (2,247∼2,388 $/kW), which is 10%∼18% lower than that of other Generation III NPPs, including AP1000 reactors and EPRs ( Liang, 2017 ). The economic competitiveness and notable mitigation capability of this nuclear technology could promote the popularity of HPR1000, especially the export of such technology to regional developing countries under major international initiatives ( Kocak et al., 2023 ). Cárdenas et al. (2023) revealed that the current high cost of nuclear electricity generation over wind power is disadvantageous for nuclear power grid penetration. Research and development of new equipment as well as redundancy design concepts for first-of-a-kind (FOAK) units could result in high equipment procurement costs, accounting for 40–50% of the total construction cost of Zhangzhou NPP Units 1 and 2 ( Liu et al., 2022 ). With the design optimization and technological maturity of nth-of-a-kind (NOAK) units, the investment of HPR1000 could be lowered. As China’s dominant reactor type in the future with competitive economics, the increased participation of HPR1000 could play a very significant role in reducing carbon emissions resulting from electricity generation ( Pereira and Posen, 2020 ).

7 Conclusions and implications

In this paper, we critically reviewed carbon emissions of NPPs. The results showed that the carbon emissions of LWRs, PWRs, BWRs, HWRs, FRs, FBRs, and GCRs are 6∼60, 2∼337.42, 11∼24.2, 3.2∼66, 9∼46.52, 2.33∼6.26, and 8.35 g CO 2 /kWh, respectively. The disparity of the review results originates from the varying emissions at the respective stages of the nuclear fuel cycle, technology choices at each stage and adopted accounting methods and boundaries. Within the nuclear fuel cycle, the emissions at the front end, including mining, milling and enrichment, are prominent. Implementing the TTC strategy is advantageous in carbon mitigation in the front end without exerting unfavorable impacts on the total cost of nuclear power generation. Emissions resulting from NPP construction and operation are underestimated due to the limited accounting methods and lack of detailed input data. An integrated CACO-NPP framework was proposed to comprehensively evaluate carbon emissions resulting from NPP construction and operation. Adopting China’s Generation III NPP with the latest technology HPR1000 as a case study, we applied the CACO-NPP framework to estimate the emissions resulting from vegetation deterioration, equipment manufacturing and labor input, which were not considered in previous studies. The total emissions of these sources reached 1,232.91 Gg CO 2 with a carbon intensity of 1.31 gCO 2 /kWh. Considering its competitive techno-economic characteristics over other mainstream Generation III NPPs, HPR1000 is both affordable in supplying base-load electricity and conducive to reducing China’s carbon emissions. As China’s future dominant nuclear power reactor, with its technological maturity and successive design improvements, the carbon emissions of HPR1000 reactors could be further reduced, playing a more vital role in realizing China’s dual carbon goal.

It is imperative to adopt measures in the nuclear fuel cycle to decrease emissions and improve the true emission reduction capability of NPPs. Underground excavation or in situ leaching is advocated for uranium mining and milling, while gas centrifugation is more “carbon friendly” than gas diffusion in the enrichment process. Moreover, the source of electricity for NPPs is very important, especially for energy-intensive processes, including mining, milling and enrichment. Furthermore, as mentioned in Section 6 , much materials, energy and personnel are needed in NPP construction and operation, and the mitigation capability of NPPs could be elevated by implementing measures in the NPP construction and operation process. During the preconstruction period, the potential vegetation carbon loss should be considered during site selection, while during the construction period, the amount of materials and energy used could be lowered by reducing design redundancy, conducting design optimization, and narrowing the construction period. Moreover, during the operation period, 5G technology and artificial intelligence (AI) are recommended to empower the digital transformation of NPPs so that they can be operated much smarter with lower personnel dependence and emissions accordingly.

The TTC mode is ecologically friendlier than the OTC mode due to the lower energy consumption and corresponding carbon emissions in the front end. Moreover, although reprocessing is involved, there are no unfavorable economic impacts on the power generation costs in the TTC strategy. Since global nuclear power development could result in an increasing uranium resource demand ( Gao and Ko, 2014 ), adopting the TTC strategy could contribute to both nuclear and ecological sustainability, which is also a feasible path for nuclear development in China within the context of its commitment to emission mitigation and promoting nuclear power generation.

China has committed itself to achieving the carbon peaking and neutrality targets through electricity and clean substitution efforts. Nuclear power is a viable option for China to pursue such ambitions with notable disincentive effects on carbon emissions and without negative impacts on the national economy ( Liu et al., 2022b ; Wang et al., 2023b ). As of the end of 2021, China was home to 53 NPPs with a total electricity generation of 407.14 billion kWh ( Department of Energy Statistics and National Bureau of Statistics, 2021 ), accounting for approximately 15% of the total non-fossil fuel power generation ( The State Council Information Office of the People’s Republic of China, 2021 ; Zhang et al., 2021 ). It is expected that nuclear power generation will reach 970∼1,000 billion kWh in 2030 ( Wen and Diao, 2022 ). Since PWRs will remain the dominant nuclear power reactor type in China for the foreseeable future, according to the reviewed average carbon emissions originating from PWRs (42.94 g CO 2 /kWh) as well as the carbon emissions resulting from fossil energy use in China (900∼1,000 g CO 2 /kWh) ( Xuan, 2020 ), the substitution of nuclear power with fossil electricity could reduce emissions by approximately 349∼390 Tg CO 2 and 831∼957 Tg CO 2 at present and by 2030, respectively. Based on the latest report of China’s National Greenhouse Gas Inventory, the total carbon emissions reached 12.3 billion tons CO 2 ( Wei et al., 2015 ). The mitigation capability of NPPs could counteract 3% of China’s total carbon emissions in 2021, which could increase to 8% in 2030. Therefore, nuclear power has a very high potential for emission reduction and could contribute to the realization of China’s carbon peaking and neutrality targets.

Author contributions

BL, BP, JH, XS, and PJ reviewed the carbon emissions of NPPs and identified the influencing factors. BL, FL, and MB established the CACO-NPP framework. LZ, WL, YZ, and XZ accounted for carbon emissions resulting from equipment manufacturing, vegetation carbon loss and labor input. BL, GL, FL, BP, and LZ wrote the manuscript. All authors contributed to the article and approved the submitted version.

Financial support was provided by the R&D project of China Nuclear Power Engineering Co., Ltd. (KY22246), National Natural Science Foundation of China (71904141, 72174192), Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (RCEES-TDZ-2021-8), and Major Research Program of Hebei Province Grants (21373902D).

Conflict of interest

Authors BL, JH, MB, XS, and YZ were employed by the company China Nuclear Power Engineering Co., Ltd.

The authors declare that this study received funding from China Nuclear Power Engineering Co., Ltd. The funder had the following involvement in the study design, data collection and analysis, decision to publish.

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

Publisher’s note

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

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenrg.2023.1147016/full#supplementary-material

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Keywords: nuclear power plants (NPPs), carbon emissions, CACO-NPP, HPR1000, carbon neutrality target

Citation: Liu B, Peng B, Lu F, Hu J, Zheng L, Bo M, Shang X, Liu W, Zhang Y, Zhou X, Jia P and Liu G (2023) Critical review of nuclear power plant carbon emissions. Front. Energy Res. 11:1147016. doi: 10.3389/fenrg.2023.1147016

Received: 07 February 2023; Accepted: 26 July 2023; Published: 19 September 2023.

Reviewed by:

Copyright © 2023 Liu, Peng, Lu, Hu, Zheng, Bo, Shang, Liu, Zhang, Zhou, Jia and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Li Zheng, [email protected] ; Gengyuan Liu, [email protected]

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Cs measures for nuclear power plant protection: a systematic literature review.

1. Introduction

2. research method, 2.1. objectives and scope, 2.2. review protocol, 2.3. literature search, 2.4. practical screening, quality assessment, and selection of studies, 2.5. data extraction and monitoring, 2.6. composing the review, 3. related work, 4. literature review, 4.1. critical digital assets in nuclear power plants, 4.2. risk assessment and threat analysis for npps, 4.3. measures for npp protection, 5. discussion, 6. conclusions, 7. future research, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Chowdhury, N. CS Measures for Nuclear Power Plant Protection: A Systematic Literature Review. Signals 2021 , 2 , 803-819. https://doi.org/10.3390/signals2040046

Chowdhury N. CS Measures for Nuclear Power Plant Protection: A Systematic Literature Review. Signals . 2021; 2(4):803-819. https://doi.org/10.3390/signals2040046

Chowdhury, Nabin. 2021. "CS Measures for Nuclear Power Plant Protection: A Systematic Literature Review" Signals 2, no. 4: 803-819. https://doi.org/10.3390/signals2040046

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Estimating the economic cost of setting up a nuclear power plant at Rooppur in Bangladesh

Gour gobinda goswami.

1 Department of Economics, North South University, Plot 15, Block B, Bashundhara, Dhaka 1229 Bangladesh

Umama Rahman

2 Department of Management, North South University, Plot 15, Block B, Bashundhara, Dhaka 1229 Bangladesh

Mehdi Chowdhury

3 Department of Accounting, Finance, and Economics, Bournemouth University Business School Dorset House Talbot Campus, Fern Barrow, Poole, BH12 5BB UK

Associated Data

The dataset is available at Harvard Dataverse: https://doi.org/10.7910/DVN/UGJCUW

Bangladesh government is in the final stage of setting up one nuclear power plant with two units at Rooppur, Ishwardi, each having 1200 MW capacity, to be launched in 2023 to meet the energy shortage urgently. The financial cost of the project is the US $12.65 billion. The primary purpose of this paper is to calculate the economic cost of setting up this plant by using the estimation method developed by Du and Parsons ( 2009 ), MIT ( 2003 ; 2009 ; 2018 ), and Singh et al.  ( 2018 ). It has been found that the economic cost is amounted to 9.36 cents/kWh for the capacity of 2400 MW. In contrast, for a similar plant in Kudankulam, Tamil Nadu, India, the corresponding cost figure is 5.36 cents/kWh for 2000 MW. Even though it seems costlier than India, the study suggests that policymakers should prefer nuclear power, as it is cost-competitive, considering the production cost of other electricity facilities. The main advantage of nuclear power is cost-competitive baseload power generation with zero carbon emission. This nuclear power plant (NPP) project is expected to boost the energy sector of Bangladesh by transforming the country from an energy deficit country into an energy surplus country.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11356-021-18129-3.

Introduction

In 2018, Bangladesh fulfilled the eligibility criterion for graduation from least developed country (LDC) list. Bangladesh’s government has a target to reach the status of a high-income country by 2041, which will increase the demand for electricity in the industrial sector substantially in years to come (Ministry of Power 2016 ). Therefore, adequate energy supply in general and specifically electricity supply will be instrumental in ensuring the country’s economic progress in the coming decades. Currently, Bangladesh is producing electricity mainly by utilizing natural gas, but the diminution in natural gas production is a significant concern for future electricity supply. Furthermore, the current demand–supply gap in the electricity sector is another major cause of concern. According to the Bangladesh Power Development Board, per capita electricity generation in Bangladesh is 484 kWh (including captive production), where 90% (including the off-grid renewables) of the population have access to electricity (Hydrocarbon Unit, Ministry of Power, 2019 ). However, the World Development Indicator ( 2019 ) estimated that 15% of the population is still deprived of electricity, while all developed countries have ensured 100% access to electricity. India, our nearest neighbor country, has 95.24% access to electricity. Therefore, Bangladesh must meet this gap and ensure that 100% of the population has access to electricity.

In 2017–2018, the growth in electricity production was 19.02%, indicating that Bangladesh is experiencing a rapid increase in electricity production. However, with a rapid rise in economic activity measured by a real GDP growth rate of around 8%, which is higher than her South Asian counterparts like India and Pakistan, electricity demand has increased concurrently.

The contraction of natural gas, combined with the growing electricity demand, has resulted in a significant thrust to generate electricity from other sources. The Bangladesh government has taken various initiatives for energy diversification and a robust, high-quality power network to maintain an uninterrupted electricity supply. Electricity generation from the nuclear power plant is one of the vital steps of the Government’s commitment to high growth and a smooth supply of electricity at a larger scale. Nuclear power offers an environment-friendly baseload power generation. The land requirement for a nuclear power plant is low and does not need natural resources, i.e., coal, natural gas, or oil. It ensures an uninterrupted power supply for a long time with zero carbon emission and with grid stability. Thus, nuclear power is crucial for fuel diversification of electricity production.

Given this advantage, Bangladesh started constructing the first nuclear power plant in Rooppur in November 2017. The plant will have two units, and the first unit is expected to commence electricity supply by 2023. The second unit is expected to start its operation in 2024. Nuclear power has a very high initial investment cost, with substantial technical complexity and significant technological, market, and regulatory risks. Still, it can supply a large amount of baseload electricity at a low operating cost (Kennedy 2007 ). The construction and operation cost of a nuclear power plant (NPP) may depend on the type of nuclear reactor used and the fuel used in its reactors (Ramana ( 2007 ); Kennedy 2007 ; Singh et al. 2018 ). However, the risk associated with NPP is very high, and the safety of waste disposal of NPP is a fundamental concern (Ministry of Power 2016 , World Nuclear Association 2018 , IAEA, 2018 ). The waste disposal cost is another essential cost component of an NPP (Ministry of Power 2016 ; Islam and Khan 2017 , & Harris et al. 2013 ). Bangladesh government, policymakers, and researchers are actively looking for a mechanism to determine the total cost of nuclear production. As Bangladesh has just started its NPP, she may face different technical complexity, regulatory issues, and required costs to train her technical personnel (Hydrocarbon Unit, Ministry of Power, 2019 ). Therefore, along with the capital, operating, and waste disposal costs, Bangladesh will also incur external costs. Electricity generation from NPP will be a better option if the cost of electricity production is competitive. Thus, it is vital to analyze the economics of the NPP for Bangladesh for expanding electricity generation through nuclear energy.

It is challenging to conduct a cost-based analysis to examine the economics of NPP and to find whether the cost of production is competitive relative to other types of energy generation. In South Asia, India has the highest number of nuclear electricity plants and in 2016 produced 2.6% of its electricity from nuclear sources. India has seven established NPPs with twenty-two reactors, and nuclear power is the fifth-largest source of electricity supply in India (World Nuclear Association, 2018b ). This study examines the cost of Rooppur units 1 and 2 with India’s Kudankulam units 3 and 4, as both of these NPP are under Rustom, a Russian NPP construction firm.

This study examines the economics of NPP using a financial model to estimate the levelized cost of electricity (LCOE) in both countries. Singh et al. ( 2018 ) define levelized cost of electricity (LCOE) as the net present value of the project’s total cost over the whole life cycle of the plant divided by the discounted quantity of electricity produced over the plant’s lifetime. This study utilizes the financial model used by Du and Parsons ( 2009 ), MIT ( 2003 ), and MIT ( 2009 ) to estimate the LCOE of Bangladesh in relationship with India, where India is considered as a benchmark. After estimating LCOE, we conduct a sensitivity analysis to reveal how different cost parameters affect the LCOE calculations in the two countries. The input cost parameters are overnight cost, operation, maintenance cost (O&M cost), decommissioning cost, fuel cost, and financial parameters: tax rate, cost debt, cost depreciation, and weighted average cost of capital.

The research findings suggest that the cost of nuclear power will be competitive in Bangladesh compared to other power generation facilities. Furthermore, it will also be competitive with other countries in the world. Moreover, according to our findings, nuclear power is competitive compared to other electricity generation facilities in India. We found that the economic cost is estimated to be 9.36 cents/kWh for the capacity of 2400 MW, whereas for a similar plant in Kudankulam, Tamil Nadu, India, the corresponding cost figure is 5.36 cents/kWh for 2000 MW.

We select a few countries like China, India, Japan, Pakistan, the UK, and the USA and compare them with Bangladesh to look at nuclear energy and other alternative uses as percentages of total energy use. We see that Bangladesh is lying at the lowest level concerning the other six countries, ranging from 2010 to 2014 (Table ​ (Table1 1 ).

Nuclear energy and other alternative use as a percent of total energy use

Source: The World Bank ( 2020 )

Table ​ Table1 1 shows that the USA is the leading country, followed by the UK. However, Japan has drastically reduced its use from 16.66 to 2.22%. The USA and the UK are still maintaining their percentage above 11% on average. Pakistan leads with above 4% in South Asia, whereas India has a figure above 2.5%. China has a double percentage figure compared to India. Bangladesh could not even reach the level of 1%. The table shows that Bangladesh can significantly improve its position in nuclear-based energy exploration.

We show the per capita electric power consumption in the same comparative setup in Table ​ Table2. 2 . The per capita electric power consumption is the lowest in Bangladesh compared to other selected countries. However, the access to electricity as a percentage of the population is above only of Pakistan. The figure is far below the global standard, and Bangladesh is also lagging behind India in this respect.

Per capita electric power consumption and access to electricity as percent of population

Source:The World Bank ( 2020 )

We report the electricity production of the selected countries in Table ​ Table3. 3 . Bangladesh lags behind other countries in electricity production from oil, gas, and coal sources (mainly fossil fuel-based production). Furthermore, Bangladesh, India, and Pakistan have an increasing trend of electricity production from oil, gas, and coal sources, whereas China, the USA, and the UK have a diminishing trend in production from fossil fuel-based sources. Meanwhile, hydroelectric sources of electricity production have decreased for Bangladesh, whereas electricity production from renewable sources increased, but its share is small compared to the overall electricity production. Bangladesh’s position in nuclear production is entirely nil at this stage, whereas there is ample opportunity to tap this channel and achieve rapid and environmentally friendly economic growth.

Electricity production from different alternative sources

The above analysis shows that Bangladesh is significantly lagging behind other countries in nuclear energy generation. The facts and figures, therefore, justify the endeavor of Bangladesh to develop a nuclear power plant. Even though the policymakers have a guideline about the expected financial cost of setting up NPP at Rooppur, Bangladesh, no economic cost–benefit analysis has so far been conducted. This paper has attempted to fill this gap through a comprehensive economic cost analysis based on the standard LCOE method developed by Du and Parson ( 2009 ), MIT ( 2003 ; 2009 ; 2018 ), and Singh et al. ( 2018 ). We use a different method used by Islam and Bhuiyan ( 2020 ) in assessing the economic cost of nuclear in Bangladesh. Islam and Bhuiyan ( 2020 ) used Financial Analysis of Electric Sector Expansion Plans (FINPLAN) modeling according to International Atomic Energy Agency (IAEA) 2018 , to estimate levelized unit electricity cost (LUEC), net present value (NPV), internal rate of return (IRR), and payback period (PBP) for nine different cases.

On the other hand, this current study provides a detailed LCOE estimation model for Bangladesh and Indian NPP. This economic costing will be immensely useful for the energy pricing of Bangladesh for commercial and other purposes and determine whether the pricing is economically viable or not. Therefore, this paper has direct policy implications for cost-effectively designing the energy pricing strategies of the Bangladesh government. Furthermore, the different cost input parameters of Islam and Bhuiyan ( 2020 ) are distinct from ours as the model is different. Another unique contribution of our paper is that it includes the external cost of the first establishment of NPP in Bangladesh as an essential key cost parameter where the previous study did not have such cost input parameters. 1

With this end in view, the study is organized as follows. The “ Literature review ” section provides a brief literature review; “ The model used in the cost calculation ” section introduces the model for cost estimation; the “ Estimation of the cost parameters of the model ” section presents the estimated results of LCOE; the “ I nterpretation of the cost estimation result” section provides a detailed interpretation of the result; the “ Sensitivity test result ” section presents a sensitivity analysis; the “ Relevance for the estimated cost ” section identifies the relevance for the cost estimation, followed by conclusion and policy suggestions in the “ Conclusion and policy implications ” section.

Literature review

Historically, nuclear power is not cost-competitive compared to fossil-fuel electricity or renewable electricity. Therefore, until today, the cost is a critical concern in the expansion of nuclear power. However, the cost is more straightforward to quantify than the benefits side, which is expected to occur in the future. Different studies examine the various aspects of the cost of nuclear power to understand the economics of nuclear power. The literature of cost analysis of NPP can be categorized into three groups: the Monte Carlo estimation method; Real option-based analyses, and standard LCOE-based analyses. In this section, we discuss these three lines of materials one after another. Among all the studies, MIT ( 2003 ), MIT ( 2009 ), MIT ( 2018 ), Wealer et al. ( 2018 ), Du and Parsons ( 2009 ), De Roo and Parsons ( 2011 ), Rothwell ( 2006 ), Singh et al. ( 2018 ) are noteworthy.

Monte Carlo estimation method

Monte Carlo simulation is a stochastic method in which the same experiment (i.e., several thousand to millions) is repeated. Different pre-defined variables are chosen from a specific range based on an assumed distribution for each trial. To understand NPP economics, some studies used the Monte Carlo estimation method to stimulate NPV or LCOE of the power plant to examine the likelihood of achieving a certain level of NPV or LCOE (Wealer et al., 2018 ). Based on the Monte Carlo simulation result, the decision can be made whether an NPP will be economically cost-competitive in the long run.

Wealer et al. ( 2018 ), using the Monte Carlo method, argue that the NPP was never an economically viable option to produce electricity. Historically, NPP has higher construction costs than its fossil-fuel counterparts, i.e., coal and natural gas. Moreover, it is still not cost-competitive with a new advanced nuclear reactor system either with renewables or fossil-fuel-based electricity. Therefore, they analyze the private investors’ perspective on generic Gen III/III + reactors with 1600 MW capacity, based on data from Europe and the USA. The study results suggest that due to a negative NPV and high LCOE, a private investor cannot invest in nuclear power compared to other electricity production options. It is to be noted that this study does not include data from China and Russia due to the unavailability of data in those countries. Thus, Wealer et al. ( 2018 ) might have come up with a different finding if they could have added Chinese nuclear electricity production in their Monte Carlo estimation, as Yu et al. ( 2020 ) argue that compared to other clean energy options, nuclear power is cost-competitive in China. According to this study, in 2017, the price of nuclear electricity was slightly higher than coal and hydropower in China, whereas it is lower than solar, wind, and biomass. Therefore, adding China to the Monte Carlo simulations may give a different conclusion for nuclear power generation cost.

Real option-based estimation method

The real option-based analysis uses the option valuation of an asset considering the uncertainties of investment. In NPP, real option-based analysis is used to examine the risk-adjusted cost of capital and the net present value taking into account net revenue uncertainties (Rothwell 2006 ). Real option value analysis can also be helpful to recognize the sensitivity of different fossil fuel prices.

Rothwell ( 2006 ) used a real option-based analysis to examine the prospect of a newly established NPP. This study attempted to determine a risk premium based on the net revenue uncertainty. It identifies that the net revenue (revenue before the payment of construction expenditure) is associated with three risks: price risk, output risk, and cost risk in a deregulated electricity market. This study measures the risks and determines how each of the risks individually and jointly influences the risk-adjusted cost of capital. Finally, this study recommends that giving risk premium and contracting can mitigate a newly established NPP’s risks and uncertainty.

Levelized cost of electricity (LCOE)-based method

In addition to the Monte Carlo estimation method and real option-based cost modeling, levelized cost of electricity (LCOE) is the most widely used method to examine the economics of nuclear power. Different studies used LCOE-based cost modeling to understand the economics of nuclear power. Among them, MIT ( 2003 ), MIT ( 2009 ), MIT ( 2018 ), Du and Parsons ( 2009 ), De Roo and Parson ( 2011 ), and Singh et al. ( 2018 ) are noteworthy. In this LCOE based method, the net present value of the total cost of an NPP is calculated, and then it is divided by the total amount of electricity produced over the plant’s life span.

MIT ( 2003 ) is the first interdisciplinary research in MIT’s future nuclear power research, introducing the standard LCOE-based analysis for nuclear power generation. This study introduces a standard and detailed levelized cost (LCOE) model for electricity generation from nuclear power, using different cost parameters. Later, MIT ( 2009 ), Du and Parson (2009), and MIT ( 2018 ) introduced a new standard in the nuclear cost analysis with a set of updated cost parameters due to change in various cost components. MIT ( 2003 ) calculates the LCOE of a hypothetical 1000 MW nuclear power plant, compares it with 1000 MW coal and natural gas power plants, and examines the cost competitiveness of NPPs. The findings suggest that nuclear power is not cost-competitive in a deregulated electricity market than other fossil fuel alternatives. According to this study, the LCOE of nuclear power, coal, and natural gas are 6.7 US Cents/kWh, 4.2 US cents/kWh, and 3.8 US cents/kWh, respectively.

MIT( 2009 ) and Du and Parsons ( 2009 ) use the same LCOE-based methodology and update all the cost parameters of MIT ( 2003 ) based on the change in the cost of construction. MIT ( 2003 ) considers the 2002 price level, whereas these two studies use more recent 2007 price levels for the cost components. Overall findings suggest that the LCOE of NPPs increased when the capital cost of construction doubled.

MIT ( 2018 ) attempts to examine the future of nuclear power in decarbonizing the electricity sector. This study exclusively focuses on new generation nuclear reactors and their cost estimation, where MIT ( 2003 ) and MIT ( 2009 ) focus on pressurized heavy water (PWR)-based technology. It provides several recommendations to improve nuclear power’s cost-competitiveness, as due to high-cost constraints, the various benefits of nuclear power are often ignored. It suggested a shift from previous light water reactor or heavy water reactor to new generation IV rector to reduce cost, introduce appropriate CO2 emission policies that will make nuclear power competitive, and raise public awareness about the benefits of nuclear energy.

De Roo and Parsons ( 2011 ) examine the LCOE for three different types of fuel cycle: once through the cycle and twice through the cycle and fast reactor cycle. The findings suggest that LCOE is higher from a once-through fuel cycle from twice through fuel cycle as twice through cycle involves recycling fuel. Thus, recycling cost raises the LCOE as one additional cost parameter is being added with it. Further, they introduce the concept of equilibrium cost for a fast reactor cycle, when “all reactors in a given fuel cycle scheme operate at constant power and that all mass flows have reached an equilibrium.” The critical difference between equilibrium cost and LCOE is that the equilibrium cost is calculated concerning the time dimension.

In contrast, LCOE is the average cost of electricity production throughout the lifetime of a plant. Therefore, the equilibrium cost is higher than the LCOE. This is because equilibrium cost has delayed realization of cost, thus including many delayed costs that can be realized with time. Finally, this study is unique regarding the LCOE and equilibrium cost analysis for different fuel cycle processes and clearly distinguishes between LCOE and equilibrium cost.

The above literature focuses on the economics of nuclear power worldwide based on three categories. However, different studies specifically discuss the economics of India’s nuclear electricity generation using LCOE estimation. Singh et al. ( 2018 ) examine the levelized cost of electricity produced from light water nuclear reactor technology in India. This article considers Indian-specific values for taxes, depreciation, and returns on equity. Furthermore, this study develops alternative scenarios for overnight costs, fuel costs, operation and maintenance (O&M) costs, cost of debt, discount rate, and return on equity. In addition to that, this article builds a financial model to calculate the levelized cost of electricity based on the present value of total costs and the discounted value of the total quantity of electricity produced over the plant’s lifetime. Finally, this study used a once-through cycle and twice-through cycle option for light water technology. According to their findings, these two options will cost 13.93 cents per kWh and 14.13 cents per kWh, respectively.

In the case of Bangladesh, no such study examines the economics of nuclear power based on any quantitative model with one exception (Islam and Bhuiyan 2020 ). This is because nuclear power is very new to Bangladesh, and its first nuclear power plant construction is in progress. It is expected that Bangladesh will generate electricity using nuclear power by the year 2023.

Therefore, it is clear from the studies that the LCOE-based methodology is widely used to examine nuclear power economics. This method is also suitable for a newly built power plant with no data on cost parameters. Thus, this current study chooses this LCOE-based approach to examine Bangladesh’s Rooppur Nuclear Plant economics and compares it with India’s Kudankulam Power Plant. To the best of the authors’ knowledge, no such peer-reviewed work has been done to estimate Nuclear Power Economics in Bangladesh except Islam and Bhuiyan ( 2020 ). They used Financial Analysis of Electric Sector Expansion Plans (FINPLAN) modeling according to International Atomic Energy Agency (IAEA) 2018 , to estimate levelized unit electricity cost (LUEC), net present value (NPV), internal rate of return (IRR), and payback period (PBP) for nine different cases. According to their study, the levelized cost of electricity ranges from 43.8 to 82.5$/MWh for Rooppur NPP. Some other non-peer-reviewed works such as Khondker and Hossain ( 2017 ) conduct financial and economic feasibility studies of the project by considering only one set of optimistic parameters, such as a PCF of 93%, a plant lifetime of 50- years, and a discount rate of 5%, and assume 3.5 cent/ kWh LCOE to estimate the different social and economic cost–benefit ratio of the projects. A summary literature table is provided based on different cost estimation methods.

Besides different cost estimation methods, literature focusing on nuclear power plants in developing countries is very limited. In a developing country with a high population density, it is challenging to manage the construction and operation of nuclear power plants. In developing countries, the private sector cannot support nuclear power plants due to high construction costs and safety issues (Lehtonen et al., 2020 ). It is critical to examine country-specific risk allocation strategies and financing issues. Hickey et al. ( 2021 ) examine the four case studies of four countries on nuclear negotiation and their prospective solution to overcome the commercial constraints of construction. These four countries are Turkey, Egypt, Jordan, and United Arab Emirates (UAE). According to their findings, commercially viable financing and fair risk allocation are significant. The state must consider a comprehensive energy mix strategy and state sovereignty due to complex issues of joint venture ownership of NPP. Degrees of control over any nuclear program, the balance of power, and the balance of debt and equity are critically important in the political situation in the Middle East. Notably, in the case of Jordan, due to high financial and repayment of the commercial loan with Rustam, Jordan canceled its two 1000 MW VVER nuclear power plant programs (World Nuclear Association 2021 ). Instead of a big reactor project in 2018, Jordan focused on buying small module reactor (SMR) project. Ramana and Ahmed ( 2020 ) identify that SMR may be a better option for Jordan than two large reactors based on financial resources and the smaller grid capacity of Jordan. However, the problem of SMR includes finding multiple suitable locations for multiple SMR, nearby water resources to cool SMR, and higher cost of electricity generation.

Meanwhile, apart from financial competence to establish nuclear power plants, it is also essential to identify the public attitude towards nuclear energy for the future sustainability of NPP in developing countries. In this context, Gupta et al. ( 2021 ) examine the public perceptions about nuclear energy in India using a nationwide survey. The result of their multiple regression analysis suggests substantial support for nuclear energy expansion in India. The public perception about the benefits of nuclear energy offsets the potential risks where concerns about energy security and climate change correlate with support for nuclear energy. Similar results can be found in another public perception study among Turkish people. Yildrium & Gün ( 2016 ) found that climate change and environmental concern have a higher significant impact over positive public attitudes on nuclear energy instead of energy security in Turkey. Furthermore, in Pakistan, Mahmood et al. ( 2020 ) suggested that nuclear energy may be a cleaner electricity source than other fossil fuel sources if some effective measures are taken. Therefore, in developing countries with high population density and high energy demand, nuclear energy may be a better option to produce electricity despite financial constraints due to energy security, climate change, environmental concern, and positive public perception. 2

Furthermore, Nuclear power is a topic of enormous debate for energy policymakers. Gupta et al. ( 2021 ) argued that nuclear power is a viable option for emission reduction in developing countries where demand is very high compared to the supply. In developing countries, increased energy demand calls for an uninterrupted baseload electricity supply, where renewable energy may not meet that huge demand. Therefore, nuclear energy can ensure reliable, affordable, and ample electricity supply in developing countries with reduced carbon emissions. On the other hand, the drawback of nuclear power is the potential risks of accidents, waste disposal issues, requirements for highly skilled workers for operation, and long-term effects of radiation (Ho et al. 2019 ). Muellner et al. ( 2021 ) argues that the climate change effect of nuclear would be minimal in the long run. According to the existing nuclear plants, including under-construction sites globally, the maximum reduction in greenhouse gas emissions would be 2–3% (Muellner et al. 2021 ). However, Davis and Hausman ( 2016 ) argue that only one nuclear plant closure in California in 2012 caused an increase in 9 million metric tons CO2 emission over 1 year. Therefore, given all the backdrops of nuclear power plants considering the baseload energy benefit with zero carbon emission and the calculated LCOE of the current study, we can safely conclude that nuclear power may be a beneficial option for electricity in the case of Bangladesh.

The model used in the cost calculation

The current paper aims to estimate the economic cost of setting up the NPP in Rooppur. The research utilizes the LCOE by following the methodology of Du and Parsons ( 2009 ), MIT ( 2003 ; 2009 ; 2018 ), and Singh et al. ( 2018 ). However, there is some criticism against the standard levelized cost-based study to understand nuclear power economics. The main criticism of LCOE-based methodology is that risks, uncertainties, and externalities are not included in the analysis. Thus, it is hard to get a clear picture of the economics of nuclear power.

The paper utilizes the standard LCOE method despite the shortcomings described above. The real-life data are not available for nuclear power plants in Bangladesh in the context of our current study. Therefore, Monte Carlo estimation or real option-based analysis, i.e., the other two methods discussed in the literature review, is beyond the scope of this study. Thus, we focus on standard levelized cost-based methodology to analyze the economics of NPPs in Bangladesh. The analysis is compared to that of India.

We calculate the LCOE separately for the newly built nuclear plant in Bangladesh and India using the following Model and compare them. Our result of the Model is determined by the set of assumptions around different cost parameters. The paper is unique in estimating the LCOE for Rooppur NPP, units 1 and 2 in Bangladesh, and units 3 and 4 of Kudankulam NPP in India, all of which are currently under construction. 3

This study relies on previous studies as some of the cost parameters for the two power plants are unavailable. Thus, this current study assumes various cost parameters based on previous LCOE studies conducted in different countries. Detailed discussions on the assumptions of different cost parameters are given in the estimation sections.

Both projects are under construction, and we assumed a 7-year construction period with a plant life of 60 years for Bangladesh and India. It is consistent with the World Nuclear Association ( 2008 ) and Harris et al. ( 2013 ), which estimated a global average of the construction period of NPP to be within 5 to 7 years. Note that the first unit of Rooppur NPP started in November 2017 and is expected to start its operation by 2023/2024 (World Nuclear Association 2020 ). Furthermore, the development of the second unit began in July 2018 and is expected to start its operation by 2024/2025 (World Nuclear Association 2020 ). On the other hand, Kudankulam units 3 and 4 started their construction in June 2017 and October 2017, respectively, and are expected to start their operation by 2023 (World Nuclear Association 2020 ). In the case of our study, the target schedule is 6.5 years approximately. Thus, we add 6 months to cover the uncertainties (including the effect of COVID-19) and assume 7 years for the construction period.

LCOE is estimated by using the following equation:

• For Bangladesh L C O E = ∑ T = 1 7 K t + I D C 1 + r t + ∑ T = 1 7 EXc 1 + r t + ∑ 8 67 ICC 1 + r t + ∑ 8 67 OM f 1 + r t + ∑ 8 67 OM v 1 + r t + ∑ 8 67 F t 1 + r t + ∑ 8 67 RE t 1 + r t + ∑ 8 36 CD t 1 + r t - ∑ 8 23 TD t 1 + r t + ∑ 71 DCOM t 1 + r t ∑ 8 67 G n 1 + r t 1
• For India 4 L C O E = ( ∑ T = 1 7 K t + I D C 1 + r t + ∑ 8 67 ICC 1 + r t + ∑ 8 67 OM f 1 + r t + ∑ 8 67 OM v 1 + r t + ∑ 8 67 F t 1 + r t + ∑ 8 67 RE t 1 + r t + ∑ 8 36 CD t 1 + r t - ∑ 8 23 TD t 1 + r t + ∑ 71 DCOM t 1 + r t ) / ∑ 8 67 G n ( 1 + r ) t 2

Description of the parameters of the model

The key difference between Eq.  1 and Eq.  2 is the inclusion of external cost identified through the variable. In Eq.  1 for Bangladesh LCOE estimation, we added an external cost of USD 180.7 million and discounted that cost over the 7 years construction period. There is a setup cost for constructing the first NPP. This is just a one-off cost, and once the NPP starts operating, there will be regular operation and maintenance costs. For India, no such one-off cost is included in the estimation.

The total duration of the model is 67 years, where the first 7 years are considered to be the pre-construction period, and the following 60 years are considered as the plant’s operating period. Each cost parameter is associated with each of the periods of the nuclear life cycle. During construction, the two key cost parameters are construction cost and interest payment, and during operation, the key parameters are variable operating cost, discount rate, tax, and depreciation rate. The decommissioning cost of the NPP commences after the end of its operating period. At the decommissioning phase, plant facilities’ safety process, disposal, and storage induce cost decommissioning (Singh et al. 2018 ). We estimate the LCOE for the two countries using the above-mentioned Eqs.  1 and 2 . In order to maintain comparability, all the estimations are in the 2010 US dollar value. The evaluation of LCOE is greatly influenced by different input cost parameters that are discussed in the following sections. Finally, to remain consistent with other studies, we do not calculate the accident risks or any other factor interrupting the electricity supply during the plant’s lifetime.

The method involves a detailed estimation of the cost parameters, which is described in the following section.

Estimation of the cost parameters of the model

The selection of the appropriate cost parameter will be addressed in the section following our literature review and assumptions made in the previous sections. All the parameters are adjusted with country-specific values. For example, tax rate, discount rate, depreciation, and debt-equity ratios vary between Bangladesh and India. We consider the current fiscal and regulatory environment to determine different parameters for the two countries. Further, we adjust the price level and inflation for the given parameters. Methods for selecting the different cost parameters for LCOE estimation are given below:

Overnight cost

The overnight cost is a part of the capital cost. It includes construction, system cost, procurement cost, engineering cost, cost of equipment, first fuel load, and other costs (World Nuclear Association 2020 ). This kind of cost is one of the key cost components of the NPP. The share of overnight cost accounts for a significant portion of the LCOE; thus, estimation of this cost is crucial while determining LCOE (Du and Parsons 2009 ).

According to the World Nuclear Association ( 2020 ), the total capital cost of construction exclusive of interest during construction and cost escalation is 12.65 billion USD for 2400 MW of Rooppur and 6.25 billion USD for 2000 MW Kudankulam units 3 and 4. Therefore, this paper calculates $5271/KW for the Rooppur power plant while considering 2400 MW capacity and $3125/KW for Kudankulam while considering the 2000 MW capacity of the plant. In the case of India, previous studies use a similar figure for overnight cost estimations. Singh et al. ( 2018 ) use overnight cost at $3000/KW, where Bharadwaj et al. ( 2008 ) use a range of $2000–$3000/KW. However, this study uses an exact amount rather than an approximation because the overnight cost is derived from real-life data. In the case of Bangladesh, the overnight cost seems significantly higher than most of the studies as Rooppur is the first NPP of Bangladesh. Therefore, it includes the setup cost instead of already established 22 nuclear reactors in India. We use this estimated figure for the base case scenario. However, the study uses a range of overnight costs of $2500–$3750/KW for India and $4217–$6326/KW in Bangladesh in the sensitivity analysis.

The overnight cost has been distributed based on the construction schedule and discounted with a given rate. The changes in LCOE are directly proportional to the changes in the overnight cost, and these fluctuations are discussed in detail in the following sections.

Interest during construction

Interest during construction (IDC) is another essential component of our LCOE estimation. This cost represents the interest cost on funds raised to build the plant (such as loan debt or stock equity) (MIT 2018 ). This cost is incurred during the construction period when there is no operating income. Thus, this cost is included in the capital cost as a financing cost. This cost is also known as “interest during construction” (IDC) or “accumulated funds during construction” (AFD) (MIT 2018 ). In other words, this is the interest payment on the amount borrowed to finance the capital during the construction period (Singh et al. 2018 ). Different studies suggest different capital costs as IDC; however, MIT ( 2018 ) estimated IDC 20% of capital cost.

Furthermore, the World Nuclear Association ( 2020 ) suggests IDC as 30% of capital when the construction period is 5 years and increases to 40% of capital when the construction period is 7 years. On the other hand, Singh et al. ( 2018 ) estimate (for an overnight cost of $2000/KW) IDC as US$324.05 million for a 5-year construction period in India. In addition to that, Bharadwaj et al. ( 2008 ) estimate IDC as 20% of the capital cost for a 5-year construction period, where Bharadwaj et al. ( 2006 ) measured 25% of the capital cost as IDC. However, we have chosen 40% of capital cost as IDC due to the 7-year construction period, for India and Bangladesh, following the World Nuclear Association ( 2020 ).

Operations and maintenance costs (O&M)

Compared to coal, natural gas, and other electricity generation facilities, the advantage of nuclear power is the low cost of O&M (Du and Parsons 2009 ). This cost solely depends on the NPP’s type of reactor and technology (Singh et al. 2018 ). Due to the unavailability of data for cost parameters, we modified the O&M cost used in MIT ( 2009 ) and Du and Parsons ( 2009 ) input parameters. Unlike Singh et al. ( 2018 ), we estimate the fixed and variable operation and maintenance costs separately, following MIT ( 2003 , 2009 ), Du and Parsons ( 2009 ), and MIT ( 2018 ). According to our estimation, the fixed operation and maintenance cost is $92.63/kW/year, and the variable operation and maintenance cost is 0.69 mills/kWh. According to MIT ( 2009 ) and Du and Parsons ( 2009 ), these costs were $56/kW/year and 0.42 mills, respectively. On the other hand, Singh et al. ( 2018 ) and Bharadwaj et al. ( 2006 ) did not divide the operations and maintenance costs into the fixed and variable parts but instead calculated aggregate operations and maintenance costs.

One of the key benefits of an NPP is the low fuel cost compared to other electricity-generating facilities. According to Du and Parsons ( 2009 ) and MIT ( 2009 ), fuel cost is 0.67 $/MMBtu, where Singh et al. ( 2018 ) calculate this cost at 0.69 cents/kWh. Therefore, following Du and Parsons ( 2009 ) and MIT ( 2009 ), we collect the 2018 price of uranium from EIA, which is 0.68 $/MMBtu, and use this price to estimate respective LCOE for India and Bangladesh.

Incremental capital cost (ICC)

Incremental capital cost is calculated as operating cost following MIT ( 2003 , 2009 ) and De Roo and Person (2009) model. This cost was added with decommissioning costs and discounted over time. In our study, we calculate the incremental cost as 1% of the overnight cost for India and Bangladesh, following MIT ( 2003 , 2009 ) and Du and Person (2009).

Tax benefit of depreciation

We assume a 7-year construction schedule with two separate depreciation schedules for the two countries. Depreciation provides a tax shield; thus, calculating the depreciation schedule while estimating the LCOE is essential. MIT ( 2003 , 2009 ) and Du and Parsons ( 2009 ) estimate a 15-year modified accelerated cost recovery system (MACRS) depreciation schedule, while this study uses a 10% salvage value, and the remaining 90% is distributed throughout the 60 years plant life of the NPP. In the case of Bangladesh, we estimate the rate of depression and schedule following the Bangladesh Power Development Board ( 2018 ) and Bangladesh Energy Regulatory Association ( 2016 ). Wee calculated a 3.28% depreciation rate for the first 10 years, and from the 11th year until the 60th year, the remaining 90% is evenly distributed. 5 In contrast, in the case of India, we directly follow the depreciation rate and the schedule given in Singh et al. ( 2018 ). According to them, a 5.28% rate is applicable for the first 12 years, and the remaining 90% is evenly distributed from the 13th year to the 60th year.

Decommissioning cost

We estimate 10% of the overnight cost as the decommissioning cost for Bangladesh, following the World Nuclear Association ( 2020 ) due to the unavailability of real-life data. According to our estimation, the decommissioning cost is $527 million for Bangladesh. On the other hand, in the case of India, we follow Singh et al. ( 2018 ) to estimate the decommissioning cost at $340 million. We estimate a separate decommissioning cost because it primarily depends on country context, reactor type, and plant size (Singh et al. 2018 ). Therefore, in the context of India, this study follows Singh et al. ( 2018 ), which provides an estimation of approximately 10% of the overnight cost.

Inflation rate and escalation factors

We estimate 6% for Bangladesh and India based on the last 5 years’ inflation rate for these two countries. Most of the studies followed MIT ( 2003 , 2009 ) and Du and Parsons ( 2009 ), using a 3% inflation rate; however, 3% inflation is not appropriate in real-life data in India and Bangladesh. On the other hand, following Singh et al. ( 2018 ), MIT ( 2003 , 2009 ), Du and Parsons ( 2009 ), we assume 1% real escalation in O&M and 0.5% real escalation in fuel cost.

Cost of debt, return on equity, and weighted average of capital

A debt-equity ratio of 90/10 was agreed between Bangladesh, Rooppur NPP, and Russian company Rustom (World Nuclear Association 2020 ). In contrast, following the Bangladesh Energy Regulatory Association ( 2016 ) estimation, we assume a return on equity of 20% and the cost of debt as 12.90% in the context of Bangladesh. Thus, combining these rates gives us a weighted average cost of capital (WACC) of 9.26%, which is used to estimate the project’s after-tax cash flows to yield the net present value. On the other hand, in the case of India, we assume an 85/15 debt-equity ratio according to their agreement with Rustom. Moreover, we calculate return on equity is 23.48%, and the cost of debt is 8%, following Singh et al. ( 2018 ). Thus, it implies a WACC of 7.94% in the case of India’s LCOE estimation.

MIT ( 2003 , 2009 ) and Du and Parsons ( 2009 ) assume the income tax rate as 37% for the LCOE estimation. Furthermore, we follow Singh et al. 2018 ) in India and estimate a 34% income tax rate for NPP. We determine a 37.5% tax rate for Bangladesh Energy Regulatory Association ( 2016 ) guideline.

External cost

Since Bangladesh has just started its first NPP, it incurs some external costs during its pre-construction and construction phase. Therefore, we estimate an external cost of USD 187.5 million for Bangladesh while evaluating the LCOE based on different setup cost calculations.

The following two tables represent a year-wise construction schedule and the estimated cost parameters for the two countries (Tables ​ (Tables4 4 and ​ and5). 5 ). The dataset is available in the repository of Harvard Dataverse at https://doi.org/10.7910/DVN/UGJCUW . This will help all the researchers and reviewers to replicate all the results used in the paper.

The base case input parameters for Bangladesh

Source: Authors’ calculation

The base case input parameters for India

Interpretation of the cost estimation result

The tables and graphs discussed in this section are calculated using Du and Parsons ( 2009 ) spreadsheet model of LCOE estimation. We first discuss the result of the base case analysis, following the discussion of the sensitivity analysis result. Our baseline cost model results suggest that the LCOE of Bangladesh is 9.35 US cents/kWh considering a 2400 MW capacity of Rooppur NPP. Our result is similar to the JICA’s estimation of 9 US cents/kWh using the 2010 US dollar (Table ​ (Table6). 6 ). On the other hand, this cost is also identical to the findings of MIT ( 2003 , 2009 ), Du and Parsons ( 2009 ). Therefore, it indicates that the LCOE in Bangladesh is at par with other countries in the world.

Comparison of LCOE for Bangladesh Rooppur NPP and Kudankulam NPP

Furthermore, we also examine the Kudankulam NPP of India, and the results indicate that for 2000 MW capacity (which is the total capacity of Kudankulam 3 and 4), the LCOE is 5.36 cents/kWh.

Therefore, our results clearly show that India’s LCOE is lower than Bangladesh for several reasons. First, according to the agreement, the construction cost is more than double in Bangladesh compared to India. Further, Bangladesh incurs an additional external cost of USD 187.5 million. Bangladesh will establish its first NPP, so it faces a setup cost for different facilities, i.e., telecommunications, transportation, water line establishment, and gridline establishment. In contrast, Kudankulam 3 and 4 will be India’s 25th and 26th nuclear power reactors. Figure  1 represents the percentage of key cost components in the LCOE estimation for Bangladesh and India. In the case of Bangladesh, the external cost has a 5% share in total LCOE estimation where the share of the capital cost is 76%, share of IDC and decommissioning cost is 3%, the share of non-fuel O&M cost is 7%, and combined share of fuel cost and the waste fee is 9%, respectively.

Percentage distribution of the key cost components in LCOE estimation in Bangladesh and India.

Source: Own calculation from the model data

Meanwhile, in India, there is no external cost in percentage share of total LCOE estimation, where the capital cost share is 64%, the share of IDC and decommissioning cost together is 4%, and the share of non-fuel O&M cost is 16%. The combined share of fuel cost and the waste fee is 16% accordingly. Therefore, it is evident that the share of external cost has a significant role in LCOE estimation in the case of Bangladesh.

On the other hand, in the case of India, LCOE is pretty competitive and similar to the findings of Bharadwaj et al. ( 2006 ) and Bharadwaj et al. ( 2008 ). In contrast, Singh et al. ( 2018 ) estimated an exceptionally high LCOE for NPP in their recent study. This is because, in the case of India, our study considers 2000 MW (two units together) capacity with a given overnight cost according to the agreement with Rustom, Russia, while other studies assume a theoretical 1000 MW capacity for one unit, for a range of overnight cost of $2000–$3000 KW. We calculate the actual per kW overnight cost according to the agreement between Russian Rustom and India. Therefore, we find a more competitive cost of NPP for India compared to other studies. Our overall result detects economics of scale in the production of nuclear power electricity in both Bangladesh and India.

As discussed earlier, the LCOE of nuclear power is competitive with other energy sources, given the electricity market structure of Bangladesh. However, the literature suggests that carbon tax makes the LCOE of nuclear electricity competitive even in deregulated electricity markets (Du and Parsons 2009 ; Kennedy 2007 ; Yu et al. 2020 ). Thus, if the Bangladesh government introduces a carbon tax on fossil fuel electricity production, the LCOE of nuclear energy will be more competitive under Bangladesh’s centralized and regulated electricity market.

In this context, it can be noted that the electricity market of Bangladesh, regulated centrally by the Ministry of Power, Energy, and Natural Resources (Asian Development Bank, 2020 ). The consumer side of the electricity market is represented by agricultural, residential, and industrial buyers. As mentioned earlier, the electricity demand has substantially increased in Bangladesh due to economic size, which indicates an expanding market. On the other hand, the supply side is fully controlled by the government. Bangladesh Power Development Board (BPDB) produced a significant fraction of electricity and served as the single buyer in the electricity market (Mostafa et al. 2017 ). The Bangladesh government also works with independent power producer (IPP) to produce electricity through a public–private partnership. However, BPDB buys all the electricity from all the producers and has a monopoly over transmission and distribution. BPDB is the only retail supplier that supplies electricity to consumers. Thus, currently, nuclear will be a beneficial option for the government compared to other electricity sources in production costs and energy security issues.

Sensitivity test result

Besides the base case estimation, we conducted a sensitivity analysis to examine the effect of uncertainty over different cost parameters in both countries. To examine the uncertainty of cost input parameters, we select overnight cost, fixed operation and maintenance cost, interest during construction, tax rate, inflation, and the weighted average cost of capital to deviate from its base value. For each variable, the upper limit and the lower limit are selected from various literatures to get a broader picture of a wide range of uncertainties around the base values. It is important to note that the upper and lower limits may depend on the country’s context. Thus, the following two tables represent each cost input parameter’s upper and lower values with their base values and respective LCOE (Tables ​ (Tables7 7 and ​ and8 8 ).

The result of the sensitivity analysis for Bangladesh

The result of the sensitivity analysis for India

Furthermore, Figs.  2 and ​ and3 3 show the result of the sensitivity analysis of LCOE for both Bangladesh and India.

Tornado diagram for LCOE of Bangladesh.

Tornado diagram for LCOE of India.

Our results show that changes in inflation and WACC (which work as a discount rate in the analysis) have the highest impact on LCOE. The least significant impact is induced by fixed operation and maintenance costs. The pattern of the result is the same for both India and Bangladesh. Thus, our sensitivity analysis ensures that uncertainty around different cost parameters for the given LCOE estimation model causes the same effect on the LCOE of NPP. The absolute value change of LCOE may differ from one country to another, but the impact of percentage change is similar. For example, in India and Bangladesh, the highest effect is induced by the inflation rate followed by WACC, interest during construction, overnight cost, and fixed operation and maintenance cost. In addition, it is crucial to recognize that overnight cost, WACC, tax rate, fixed operation, and maintenance cost have a positive relationship with LCOE. On the other hand, inflation has an inverse relationship with LCOE. Thus, the country with a higher inflation rate will have a lower LCOE and vice versa.

Figures  4 and ​ and5 5 clearly show how LCOE changes for both Bangladesh and India for every low and high value of the cost parameters. In the context of Bangladesh, the following result suggests that when WACC is 8% and 12%, the LCOE is 7.40 cents/kWh and 14.81 cents/kWh, respectively. It induces a 7.41 cents/kWh change in LCOE, whereas for a 3% inflation rate, the LCOE is 15.78 cents/kWh, and for a 10% inflation rate, it is 4.45 cents/ kWh. Therefore, the impact of a change in the inflation rate is larger than any other factor. On the other hand, in Kudankulam, for 5% and 10% WACC, the LCOE is 3.43 cents/kWh and 7.46 cents/kWh, respectively. Moreover, for a 3% inflation rate, the LCOE is 8.78 cents/kWh, and for a 10% inflation, it is 3.05 cents/kWh.

Levelized cost of nuclear power plant in Bangladesh for different scenarios of high-cost and low-cost parameters

Levelized cost of nuclear power plant in India for different scenarios of high-cost and low-cost parameters

Finally, our sensitivity result shows that, due to a change in fixed operation and maintenance cost for Bangladesh and India, the absolute difference between the upper and the lower values of LCOE is 0.28 and 0.35, respectively. Therefore, the operation and maintenance costs have the lowest impact on the LCOE of nuclear power estimation. A detailed simulation result for the two countries is also provided.

Relevance for the estimated cost

The findings of our study are highly relevant in the context of the electricity sector of Bangladesh. No study has so far calculated the LCOE of electricity for any fuel in Bangladesh. Table ​ Table9 9 represents the per kWh generation cost of electricity from different sources owned by the public power plant in 2018–2019 (Bangladesh Power Development Board 2020) and the electricity purchase cost for Bangladesh Power Development Board for the year 2018–2019.

Generation cost and purchase cost of electricity by fuel source (BDT/kWh)

Source: Compilation by authors from Bangladesh Power Development Board (BPDB 2020) and eighth 5-year plan of Bangladesh, 2021.

*All the generation cost is based on rates from public plant owned by BPDB.

**From independent power producer (IPP) and small independent power producer (SIPP).

***From subpublic plant.

****From rental and quick rental.

It shows that wind-generated electricity has the highest generation cost, where the lowest costs are for hydroelectricity power generation. The costs are BDT 81.88/kWh and BDT 1.00/kWh, respectively. The table, additionally, shows the cost of electricity generation using gas, coal, heavy fuel oil (HFO), high-speed diesel (HSD), and solar. The purchase cost per kWh may vary depending on the ownership of the plant. According to the table, the lowest purchase cost is for IPP- and SIPP-owned power plants for gas-generated electricity. The highest purchase cost is for HSD-generated electricity purchased from rental and quick rental. These costs are BDT 2.47/kWh and BDT 27.46/kWh, respectively. Bangladesh Power Development Board buys electricity from these producers at their prices and supplies them to different consumers using various tariff rates.

In addition, the Bangladesh government utilizes different electricity tariff rates for different consumer groups (Table ​ (Table10 10 ).

Tariff rates by different consumer categories (BDT/kWh) ***

Source: *This flat rate is the average rate calculated by the authors.

**Flat tariff rate is considered.

***All are based on low tension 230/400 V.

The rates indicate that the Bangladesh government follows a discriminatory price policy based on the need of consumers. The lowest tariff rate applies to agricultural customers (BDT 4.16/kWh), whereas office and commercial consumers pay the highest tariff rate (BDT 10.30 /kWh). There is a marginal pricing system for a different set of consumption units; thus, this study calculates a flat average tariff rate of BDT 7.90 /kWh for this group of consumers. We can understand that the Bangladesh government heavily subsidizes the electricity sector operating at two stages. First, the government subsidizes the production cost of electricity and provides further subsidies while supplying the electricity to different consumer groups. Therefore, the consumers are paying a tariff lower than the actual production cost of electricity.

Consequently, the policy implication of LCOE analysis is crucial for Bangladesh. Our findings suggest that the LCOE of Bangladesh is BDT 7.94/kWh. Hence, if the government can sell the electricity above this price, it will earn a profit. The government may yield a lower return to capital or incur a loss if the government sells electricity below this threshold level. Therefore, nuclear power can be considered cost-competitive if hydroelectric and gas production costs are lower than nuclear (Table ​ (Table9). 9 ). However, it is essential to note that per-unit production cost and LCOE follow different estimation techniques. LCOE estimates discounted revenue and cost considering the plant lifetime. Hence, the actual per-unit production cost will be much lower than the LCOE estimation. Even though we consider the LCOE of nuclear, it is still lower than imported coal, solar, HFO, HSD, and wind production cost. Thus, it is evident that nuclear will be more beneficial compared to all other sources. It is also important to note that gas is a depletable resource in Bangladesh and hydroelectricity is not a feasible option due to the characteristics of rivers of the country.

Moreover, coal emits high levels of CO2, whereas nuclear power has zero carbon emissions. Power generation through wind is an expensive option among renewables, which exhibits BDT 81.88/kWh production cost, whereas solar is a reasonable option. However, nuclear has baseload power generation that ensures uninterrupted electricity supply, whereas solar production does not ensure an uninterrupted electricity supply since it is highly dependent on weather conditions.

Finally, the subsidy amount will also be smaller than other electricity sources if we consider the tariff rate. Therefore, in Bangladesh, nuclear power is a viable energy option to have in the energy basket. Our results suggest that introducing nuclear power will increase our electricity supply at a competitive cost. Even when we compare our LCOE with India, we notice that Bangladesh may have higher LCOE, but this is because Rooppur NPP is the first nuclear power plant, and we are facing an external cost of US$187.5 million because of that. Thus, in the future, it may become more cost-efficient compared to India.

Furthermore, it is an excellent option to produce electricity in a cost-competitive manner within the country’s context. This study finds nuclear power to be an effective viable option for energy diversification, and it should be included in the energy basket of Bangladesh in the long run. Nuclear power will provide sufficient energy security and diversification, along with zero carbon emissions in Bangladesh.

Conclusion and policy implications

In Bangladesh, the increasing electricity demand is triggered by the growing size of the economy and its transformation to modernization. According to the Bangladesh government’s calculation, access to electricity is 90%, while, according to World Development Indicator, in 2018, 85% of the population had access to electricity. Therefore, 10–15% of the population is deprived of electricity facilities. Bangladesh’s government needs to establish an uninterrupted diversified power supply system to ensure 100% access to electricity and meet the growing demand for industrial activities. As mentioned earlier, it is also vital to reduce the dependence on natural gas and oil-based electricity due to the depletion of resources and the negative environmental impact. Furthermore, along with coal and solar power, nuclear power plays a vital role in Bangladesh government’s power supply master plan. The Bangladesh government believes the Rooppur mega project will maintain a secure power supply and reduce CO 2 emissions at a lower operating cost.

However, there is an increasing concern about the enormous amount construction cost of nuclear power. In Bangladesh, the cost of producing electricity is always higher than the price of electricity. According to Bangladesh government, in the last 10 years, the amount of subsidy given to the power sector was equal to BDT 522.6 billion due to higher production costs and lower selling price of electricity. Therefore, it is also critical to ensure an affordable production cost of electricity to minimize the subsidy burden. Hence, it is crucial to understand the economics of nuclear power in Bangladesh, examining the levelized cost of electricity from nuclear power plants using a standard levelized cost-based financial model. In this paper, we have made the noble attempt to conduct a thorough economic cost analysis of setting up the first nuclear power plant at Rooppur in Bangladesh by using the unique discounted present value method developed by Du and Parsons ( 2009 ), MIT ( 2003 ; 2009 ; 2018 ), and Singh et al. ( 2018 ). This paper did it uniquely in Bangladesh, soon becoming another nuclear power in South Asia after India and Pakistan.

We compared the levelized cost of Bangladesh with India to examine the broader picture of nuclear power-generated electricity. This study develops this model, including all the vital cost parameters, i.e., overnight cost, decommissioning cost, operating cost, and financial components such as interest during construction, incremental capital cost, cost debt, and the weighted average cost of capital, depreciation cost, tax rate, and others. Our assumption regarding various input parameters is based on a detailed literature review and country-specific contexts. The base case estimation suggests that the LCOE of Rooppur NPP is 9.36 US cents/kWh or BDT 7.94 per kWh (with an exchange rate of $1 = BDT 84.877, which is 0.84877 × 9.36 = 7.94). The LCOE of Kudankulam India is 5.36 US cents/kWh or 3.93 Indian Rupee/kWh (with an exchange rate of $1 = 73.4 Indian Rupee which is 0.734 × 5.36 = 3.93).

Along with base case estimation, this study conducts a sensitivity analysis on key input parameters. We use a range of values around the base values of key input parameters to see the impact on LCOE estimations. Our results suggest that the inflation rate, the weighted average cost of capital, and IDC significantly impact LCOE.

Following the findings, this paper strongly suggests that nuclear power is a worthwhile option for electricity production in Bangladesh, considering energy security, diversifications of energy basket, zero carbon emission, and cost-competitiveness. In the future, if solar and other renewables become more cost-competitive, these may compete with nuclear power. However, nuclear power will still be appealing even comparing renewables because of its baseload power generation. The drawback of nuclear in Bangladesh is its high risk of accidents, which will induce a considerable cost with a significant level of health hazard. Furthermore, without foreign investment, it will be hard for the Bangladesh government to bear the construction cost of nuclear power and technological support. Nevertheless, the latest technology ensures the minimum risk of nuclear accidents. Thus, if Bangladesh government can ensure foreign investment to build nuclear power plants, it may become an attractive option to produce electricity.

Moreover, the Bangladesh government plans to diversify its power generation to meet low-cost fuel and low carbon emission criteria. Therefore, according to the eighth 5-year plan, the Bangladesh government has revised its nuclear-produced electricity target. Currently, the government plans to produce 14% of power from nuclear sources in 2031 and 12% in 2041 (Moazzem & Shibly 2021 ). Furthermore, the Bangladesh government has taken various initiatives in the 8th 5-year plan to achieve green growth under environmental and climate change strategies. The government plans to introduce an emission accounting strategy that will make the polluters bound to pay (GED 2020 ). The government also has a plan for decarbonatization or a policy of a low carbon economy. Thus, the government has a target for low fossil-fuel use along with low-greenhouse gas emissions. Therefore, if the government can implement these plans and introduce a carbon tax in Bangladesh, nuclear will be a better option than other fossil fuel alternatives for baseload uninterrupted power supply. Meanwhile, as a part of reducing CO2 emission, the government also has a plan to increase the share of renewable use, which may work as a constraint to nuclear expansion. However, as discussed earlier in developing countries, renewable energy may not suppress the demand for nuclear electricity due to baseload uninterrupted power supply.

The electricity market of Bangladesh is highly regulated and centralized by the Ministry of Power and Bangladesh Power Development Board (BPDB). Hence, as only transmitter and distributor and supplier of electricity, nuclear electricity may be a good option in the short run. In the long run, deregulation and privatization of the power sector may take place. At that stage, carbon tax and other environmental regulations may make nuclear a profitable option compared to other electricity sources. Furthermore, nuclear technology requires highly skilled workers. Currently, Bangladesh entirely depends on Russian technological support. Hence, in the short run, this intuitional setup may work well. However, in the long run, if government wants to expand its nuclear production, it should arrange full technological support and necessary training facilities for skilled workers at the domestic level.

This study only estimates the LCOE of nuclear power in the context of Rooppur, Bangladesh, and Kudankulam, India, then compares them. Further research may explore the LCOE of other vital sources of electricity production in Bangladesh, such as coal, solar, HFO, HSD, and others. That will provide a complete picture of the cost of producing electricity in terms of LCOE in Bangladesh and help policymakers set their future energy policy and electricity production targets.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Table ​ Table11 11

Summary table for literature review table on different methods of cost estimation techniques

Source: Own compilation.

Table ​ Table12 12

Detailed simulation table for Bangladesh

Note: Highlighted cells in bold indicate the simulations of high and low values for the respective variable.

Table ​ Table13 13

Detailed simulation table for India

Author contribution

Gour Gobinda Goswami: Formulating idea, writing, reviewing, investigation, and editing, validation, supervision.

Umama Rahman: Methodology, levelized cost estimation, model, original draft preparation.

Mehdi Chowdhury: Literature review, editing, draft preparation, organization of the paper.

This study is funded by North South University, Dhaka, Bangladesh, under its internal research grant for faculty members entitled “Conference, Travel, and Research Grant,” Cycle: 2019–20.

Data availability

Declarations.

Not applicable.

The authors declare no competing interests.

1 We are grateful to an anonymous reviewer for suggesting us to add this section in the introductory part for sharpening the focus of the paper.

2 We are grateful to an anonymous reviewer for bringing up the issue of developing countries with high population density and low geographical space.

3 Unit 1 and 2 of Kudankulam are operating from 2013 and 2014, respectively.

4 The above two equations are modified from Singh et al. ( 2018 ) and Du and Parsons ( 2009 ).

5 The 90% depreciation rate and 10% salvage value are estimated following both Bangladesh Energy Regulatory Association ( 2016 ) and Singh et al. ( 2018 ). In contrast, the 10-year and 50-year schedule are following the depreciation schedule given in Bangladesh Energy Regulatory Association ( 2016 ).

• Bangladesh government is setting up two units of nuclear power plants in Rooppur with 1200 MW capacity each for the first time in its history.

• The total financial cost of this construction has already been estimated to be US$12.65 billion.

• This paper attempts to assess the broader economic cost of setting up this plant at Rooppur, Bangladesh, by using the discounted present value method developed by Du and Parsons ( 2009 ), MIT ( 2003 ; 2009 ; 2018 ), and Singh et al. ( 2018 ).

• The levelized cost of electricity (LCOE) has been estimated to be 9.36 cents/kWh, whereas the rate is 5.34 cents/kWh for a similar plant of Kudankulam Tamil Nadu, India.

• In terms of Bangladeshi currency, the LCOE is amounted to BDT 7.94/kWh. Hence, if the government can sell the electricity above this price, the project will be economically viable or profitable.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Umama Rahman, Email: [email protected] .

Mehdi Chowdhury, Email: ku.ca.htuomenruob@yruhdwohcm .

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Wellampitiya, Sri Lanka

During an IAEA review mission in Sri Lanka, experts undertook geophysical studies at a potential site for a nuclear power plant in Pulmoddai, near the Trincomalee region. (Photo: J. Gunatilake, University of Peradeniya)

An International Atomic Energy Agency (IAEA) team of experts has concluded a seven-day safety review of Sri Lanka’s selection process to identify potential sites to build its first nuclear power plant. The South Asian country is pursuing the introduction of nuclear power to increase its low carbon power production to meet energy demand, tackle climate change and increase energy security.

The Site and External Events Design Review Service (SEED) mission, which took place from 30 May to 5 June, reviewed Sri Lanka’s adherence to IAEA guidance on site selection, including exclusion and screening criteria. Sri Lanka has completed the site survey stage and identified six candidate sites from three different regions. The next phase, which is ongoing, includes evaluation, comparison and ranking studies of the candidate sites.

The SEED mission was carried out at the request of the Government of Sri Lanka and hosted by the Sri Lanka Atomic Energy Board (SLAEB) under the purview of the Ministry of Power and Energy.

The team comprised of three experts from Canada, Pakistan and Türkiye, as well as one IAEA staff. They reviewed the site survey report, together with the siting process, siting criteria, data collection process and application of the management system for siting activities. The team also visited and observed one of the candidate sites in Pulmoddai, near the Trincomalee region.

“Sri Lanka is comprehensively screening site-specific external hazards in the site selection process, while following the IAEA safety standards and adopting best practices,” said mission team leader Ayhan Altinyollar, an IAEA Nuclear Safety Officer.

The team provided recommendations to optimize the site evaluation process to select the most favourable site. In particular, the IAEA team recommended that SLAEB:

• Further align the siting process to the IAEA Safety Standard Series No. SSG-35, Site Survey and Site Selection for Nuclear Installations ; and
• Collect and incorporate additional site-specific information into the siting process.

As a good practice, the team noted that SLAEB has been conducting collaborative siting studies in an open and transparent manner with stakeholder organizations, such as the Geological Survey and Mines Bureau, Central Environmental Authority and the Department of Geology of University of Peradeniya.

“Sri Lanka has identified nuclear as a clean and green energy source to fulfil the future electricity demand in Sri Lanka. In March 2024, the Cabinet of Ministers made a strategic and knowledgeable commitment towards the country’s nuclear power planning programme. Interpretation and application of IAEA safety standards within the context of site selection for a nuclear power plant is crucial for a strong nuclear power programme in Sri Lanka,” said Professor Rexy Denzil Rosa, Chairman of SLAEB.

The mission team briefed the Secretary of the Ministry of Power and Energy, Sulakshana Jayawardhana, and the Director General of the Geological Survey and Mines Bureau, Ranjith Premasiri, about the review of Sri Lanka’s selection process and findings of the SEED mission.

Sri Lanka also hosted a national workshop on nuclear law in November 2023, as well as an IAEA Integrated Nuclear Infrastructure Review (INIR) mission in April 2022, which reviewed the country’s infrastructure development for a nuclear power programme.

The final SEED mission report will be delivered to the Government of Sri Lanka within three months.

About Site and External Events Design Review Service (SEED) missions

SEED missions  are expert review missions that assist countries going through different stages in the development of a nuclear power programme. The service offers a choice of modules on which to focus the review, such as site selection, site assessment and design of structures, systems and components, taking into consideration site specific external and internal hazards.

In the case of site selection review, SEED missions assess the appropriate consideration of the safety issues in the site selection process.

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IAEA Concludes Safety Review of Sri Lanka’s Potential Nuclear Power Plant Sites

Hosted by the Sri Lanka Atomic Energy Board (SLAEB) and under the purview of the Ministry of Power and Energy, the SEED mission was requested by the Government of Sri Lanka.

An International Atomic Energy Agency (IAEA) team of experts has concluded a seven-day safety review of Sri Lanka’s site selection process for its first nuclear power plant. The South Asian nation is aiming to adopt nuclear energy to enhance low carbon power production, address climate change, and bolster energy security.

The Site and External Events Design Review Service (SEED) mission, conducted from May 30 to June 5, evaluated Sri Lanka’s adherence to IAEA guidance on site selection, including exclusion and screening criteria. The country has identified six candidate sites from three regions following the completion of its site survey stage. The ongoing phase involves evaluation, comparison, and ranking of these sites.

The expert team, consisting of members from Canada, Pakistan, Türkiye, and an IAEA staff member, reviewed the site survey report, the siting process, criteria, data collection, and application of the management system for siting activities. They also visited and observed a candidate site in Pulmoddai, near the Trincomalee region.

“Sri Lanka is comprehensively screening site-specific external hazards in the site selection process, while following the IAEA safety standards and adopting best practices,” stated mission team leader Ayhan Altinyollar, an IAEA Nuclear Safety Officer.

The team provided recommendations to optimize the site evaluation process to select the most favorable site, including:

Further aligning the siting process with IAEA Safety Standard Series No. SSG-35, Site Survey and Site Selection for Nuclear Installations.

Collecting and incorporating additional site-specific information into the siting process.

The team also commended SLAEB for conducting collaborative siting studies in an open and transparent manner with stakeholder organizations such as the Geological Survey and Mines Bureau, Central Environmental Authority, and the Department of Geology of University of Peradeniya.

“Sri Lanka has identified nuclear as a clean and green energy source to fulfill future electricity demand. In March 2024, the Cabinet of Ministers committed to the country’s nuclear power planning programme. Interpretation and application of IAEA safety standards within the context of site selection is crucial for a strong nuclear power programme in Sri Lanka,” said Professor Rexy Denzil Rosa, Chairman of SLAEB.

The mission team briefed the Secretary of the Ministry of Power and Energy, Sulakshana Jayawardhana, and the Director General of the Geological Survey and Mines Bureau, Ranjith Premasiri, on their review of Sri Lanka’s selection process and the findings of the SEED mission.

Sri Lanka previously hosted a national workshop on nuclear law in November 2023 and an IAEA Integrated Nuclear Infrastructure Review (INIR) mission in April 2022, which assessed the country’s infrastructure development for a nuclear power programme.

The final SEED mission report will be delivered to the Government of Sri Lanka within three months.

About Site and External Events Design Review Service (SEED) missions

SEED missions are expert review missions that assist countries at various stages of developing a nuclear power programme.

The service offers modules focusing on siteselection, site assessment, and the design of structures, systems, and components, considering site-specific external and internal hazards. In the case of site selection review, SEED missions assess the safety considerations within the site selection process to ensure adherence to international standards and best practices.  

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Nuclear Energy’s Bottom Line

The United States used to build nuclear-power plants affordably. To meet our climate goals, we’ll need to learn how to do it again.

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Produced by ElevenLabs and News Over Audio (NOA) using AI narration.

N uclear energy occupies a strange place in the American psyche—representing at once a dream of endless emissions-free power and a nightmare of catastrophic meltdowns and radioactive waste. The more prosaic downside is that new plants are extremely expensive: America’s most recent attempt to build a nuclear facility, in Georgia, was supposed to be completed in four years for $14 billion. Instead it took more than 10 years and had a final price tag of $35 billion — about 10 times the cost of a natural-gas plant with the same energy output.

But the United States might not have the luxury of treating nuclear energy as a lost cause: The Department of Energy estimates that the country must triple its nuclear-power output by 2050 to be on track for its climate targets. For all the recent progress in wind and solar energy, renewables on their own almost certainly won’t be enough. Arguably, then, we have no choice but to figure out how to build nuclear plants affordably again.

Half a century ago, nuclear energy seemed destined to become the power source of the future. The first commercial-reactor designs were approved in the 1950s, and by the late ’60s, America was pumping them out at a fraction of what they cost today. In 1970, the Atomic Energy Commission predicted that more than 1,000 reactors would be operating in the United States by the year 2000.

In the popular history of atomic energy in America, the turning point was the infamous meltdown at the Three Mile Island plant in 1979. In the aftermath of the accident, environmentalists pressured regulators to impose additional safety requirements on new and existing plants. Nuclear-energy advocates argue that these regulations were mostly unnecessary. All they did, in this telling, was make plants so expensive and slow to build that utility companies turned back to coal and gas. Activists and regulators had overreacted and killed America’s best shot at carbon-free energy.

This story contains some kernels of truth. The safety risk of nuclear energy is often wildly overblown. No one died at Three Mile Island, and later studies found that it didn’t have any adverse health effects on the local community. Even including the deadly meltdowns at Chernobyl and Fukushima, nuclear power has most likely caused only a few hundred deaths, putting its safety record on par with wind turbines and solar panels, which occasionally catch fire or cause workers to fall. (The immediate areas around the sites of the Chernobyl and Fukushima disasters have, however, been rendered uninhabitable for decades because of the potential dangers of radiation.) Nuclear waste can be harmful if mishandled, but isn’t difficult to store safely. Air pollution from fossil fuels, meanwhile, is estimated to kill anywhere from 5 million to 9 million people every year.

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The claim that excessive regulation single-handedly ruined the American nuclear industry, however, doesn’t hold up. The cost of building new nuclear plants was already rising before Three Mile Island. Several nuclear-energy experts told me that a major driver of those cost increases was actually a lack of industry standards. According to Jessica Lovering, the executive director of Good Energy Collective and a co-author of a widely cited study on the cost of nuclear energy, throughout the ’60s and ’70s, utilities kept trying to build bigger, more ambitious reactors for every new project instead of just sticking with a single model. (Lovering used to be the head of nuclear policy at the Breakthrough Institute—a think tank that tends to warn against excessive regulation.) “It’s like if Boeing went through all the trouble to build one 737, then immediately threw out the design and started again from scratch,” she told me. “That’s a recipe for high costs.” The 94 nuclear reactors operating in the United States today are based on more than 50 different designs. In countries such as France and South Korea, by contrast, public utilities coalesced around a handful of reactor types and subsequently saw costs remain steady or fall.

Lovering also noted that the overregulation story leaves out a crucial fact: Because of a slowing economy, electricity demand flatlined in the early 1980s, causing American utilities to stop building basically every electricity-generating resource, not just nuclear plants. By the time the U.S. finally did try to build them again, in 2013, the American nuclear industry had all but withered away. “In the 1970s, we had a whole ecosystem of unionized workers and contractors and developers and utilities who knew how to build this stuff,” Josh Freed, who leads the climate and energy program at Third Way, a center-left think tank, told me. “But when we stopped building, that ecosystem died off.” This became obvious during the disastrous Vogtle project, in Georgia—the one that ended up costing $35 billion. Expensive changes had to be made to the reactor design midway through construction. Parts arrived late. Workers made all kinds of rookie mistakes. In one case, an incorrect rebar installation triggered a seven-and-a-half-month regulatory delay. Experts estimate that by the time it was finished, the project was four to six times more expensive per unit of energy produced than plants built in the early ’70s.

Given the impracticality of nuclear energy, some environmentalists argue that we should focus on wind and solar. These technologies can’t power the entire grid today, because the sun doesn’t always shine and the wind doesn’t always blow. With enough advances in battery-storage technology , however, they could in theory provide 24/7 power at a far lower price than building nuclear plants. “The nuclear industry has been promising cheap, clean energy for decades at this point,” David Schlissel, a director at the Institute for Energy Economics and Financial Analysis, told me. “Why waste our money on false hopes when we could be putting it towards technologies that have a real chance of working?”

He may be right about the technology. But just because it might one day be technically feasible to power the entire grid with renewables doesn’t mean it will ever be politically feasible. That’s because wind and solar require land—a lot of land. According to Princeton University’s “Net-Zero America” study , reaching net-zero emissions with renewables alone would involve placing solar panels on land equivalent to the area of Virginia and setting up wind farms spanning an area equivalent to Arkansas, Iowa, Kansas, Missouri, Nebraska, and Oklahoma combined. The more land you need, the more you run into the meat grinder of American NIMBYism. Efforts to build renewables are already getting bogged down by local opposition , costly lawsuits , and permitting delays . These challenges will only intensify as the easiest sites come off the board.

Transmission lines, which are needed to transport renewable energy from where it’s generated to where it’s used, may present an even bigger challenge. Some lines have taken nearly two decades just to receive their full suite of approvals. “There’s a chance we will suddenly get our act together and overcome the many, many constraints to deploying renewables,” Jesse Jenkins, who leads the Princeton Zero-Carbon Energy Systems Research and Optimization Lab, told me. “But I’m certainly not willing to bet the fate of the planet on that happening.”

The case for nuclear , then, is less about technological possibilities than it is about political realities. Nuclear can generate the same amount of power while using 1/30th as much land as solar and about 1/200th as much as wind. Reactors can be built anywhere, not just in areas with lots of natural wind and sunshine, eliminating the need for huge transmission lines and making it easier to select sites without as much local opposition. And nuclear plants happen to generate the greatest number of high-paying jobs of any energy source, by far. (On average, they employ six times as many workers as an equivalent wind or solar project does and pay those workers 50 percent more.) That helps explain why four different towns in Wyoming recently fought over the right to host a nuclear project. Nuclear power is also the only energy source with overwhelming bipartisan support in Washington, which makes Congress more likely to address future bottlenecks and hurdles as they arise.

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As for how to make the economics work, there are two schools of thought. One holds that if America forgot how to build nuclear because we stopped doing it, we just need to start back up. Pick a design, build lots of plants, and we’ll eventually get better. Other countries have done this with great success; South Korea, for instance, slashed the cost of constructing nuclear plants in half from 1971 to 2008. Here, the Vogtle project carries a silver lining: The second of the plant’s two reactors was about 30 percent cheaper to build than the first, because workers and project managers learned from their mistakes the first time around. “I consider Vogtle a success,” Mike Goff, acting assistant secretary for the Department of Energy’s Office of Nuclear Energy, told me. “We learned all kinds of hard lessons. Now we just need to apply them to future projects.”

The second school of thought is that we’ve been building nuclear reactors the wrong way all along. This camp points out that over the past half century, basically every kind of major infrastructure project—highways, skyscrapers, subways—has gotten more expensive, whereas manufactured goods—TVs, solar panels, electric-vehicle batteries—have gotten cheaper. Lowering costs turns out to be much easier when a product is mass-produced on an assembly line than when it has to be built from scratch in the real world every single time. That’s why dozens of companies are now racing to build nuclear reactors that are, in a phrase I heard from multiple sources, “more like airplanes and less like airports.” Some are simply smaller versions of the reactors the U.S. used to build; others involve brand-new designs that are less likely to melt down and therefore don’t require nearly as much big, expensive equipment to operate safely. What unites them is a belief that the secret to making nuclear cheap is making it smaller, less complicated, and easier to mass-produce.

Both paths remain unproven—so the Biden administration is placing bets on each of them. The president’s signature climate bill, the Inflation Reduction Act, included generous tax credits that could reduce the cost of a nuclear project by 30 to 50 percent, and the Bipartisan Infrastructure Law included $2.5 billion to fund the construction of two new reactors using original designs. The Department of Energy, meanwhile, is exploring different options for permanent nuclear-waste storage, investing in building a domestic supply chain for uranium, and helping companies navigate the process of getting reactor designs approved.

There’s no guarantee that the U.S. will ever relearn the art of building nuclear energy efficiently. Betting on the future of atomic power requires a leap of faith. But America may have to take that leap, because the alternative is so much worse. “We just have to be successful,” Mike Goff told me. “Failure is not an option.”

Support for this project was provided by the William and Flora Hewlett Foundation.