Electricity generation technologies: Comparison of materials use, energy return on investment, jobs creation and CO2 emissions reduction

Kis, Zoltán; Pandya, Nikul; Koppelaar, Rembrandt

doi:10.1016/j.enpol.2018.05.033

Cited by 46 publications

(39 citation statements)

References 49 publications

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“…Moreover, there are many factors affecting BIRC which may be more likely to interfere with each other, and the principal component analysis (PCA) is used to screen the factors to simplify the complexity of the neural network and improve the prediction accuracy. PCA has been effectively integrated with neural networks in the fields of electricity [9], agriculture [10], tourism [11], and industrial manufacturing [12], but not yet into research on environmental behavior. Furthermore, the fields mentioned above have rarely considered the regularization of neural networks when using principal component analysis and neural network models.…”

Section: Introductionmentioning

confidence: 99%

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Dong¹,

Hua²

2018

Sustainability

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While enriching people’s lives, the rapid development of online shopping has posed a severe challenge to the environment. Questionnaires focusing on the intention to recycle packaging waste are designed. These questionnaires contain first-level variables such as recycling behavior attitude, recycling behavior cognition, situational factors, historical recycling behavior, and recycling behavior intention. With the collected questionnaire data, a regression analysis is first conducted on the selection of variables and the effect of variable prediction. After ensuring the validity of the variables, 15 second-level variables are extracted into eight principal components using principal component analysis. These components serve as input to a Bayesian regularized neural network. Subsequently, a three-layer (8-15-1) neural network model is constructed; the trained neural network model achieves a high degree of fit between the predicted and measured values of the test set, thus further proving the rationality of the selected variables and the neural network model. Finally, this study uses the connection weights matrix of the neural network model and the Garson formula to analyze in depth the specific impact of each second-level variable on the intention to recycle packaging waste. Note that given the particularity of packaging waste recycling behavior, the impact on social norms, recycling behavior knowledge, values, and publicity on behavioral intentions in second-level variables is different from that obtained in similar previous studies.

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Section: Introductionmentioning

confidence: 99%

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Dong¹,

Hua²

2018

Sustainability

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“…Table 4 shows that our EROI st results are within but on the lower bound of the literature for the RES technologies excepting for CSP [14,20,34,74,94,103,108,109]. Two main reasons explain this: (1) few studies use so many materials for the different construction, O&M, and dismantling phases, and (2) it is customary in the field to use favorable performance factors, which overestimate the average of real systems (see discussion in [25]).…”

Section: Comparison Of the Eroi St Of Res Technologies With The Literaturementioning

confidence: 65%

“…This is the reason why the power degradation assigned is only an estimation of the average power loss based in the % of dam failures in Table 2. For the rest, we take from Kis et al [74] the annual degradation % of the power plants and estimate the already present power degradation based in the average operational time. The total expected power degradation in the lifetime minus the actual present degradation is the expected degradation in the pipeline that we use in Table 2.…”

Section: Materials Requirements and Performance Factors Per Technologymentioning

confidence: 99%

“…Still, we report the comparison of our obtained results for EROI st with the literature range found in the review papers and meta-analyses to show how far or near we are of other published studies. We also compare with the EROI st calculations from Dupont and Kis et al, [74,94,103] since this team follows, to the knowledge of the authors, a methodology that is the closest to the one developed here; therefore, the direct comparison of results is the most meaningful. However, for a direct comparison a harmonization process should be endeavored with relation to the assumptions and performance factors (CF, g factor, etc.…”

Section: Comparison Of the Eroi St Of Res Technologies With The Literaturementioning

confidence: 99%

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Standard, Point of Use, and Extended Energy Return on Energy Invested (EROI) from Comprehensive Material Requirements of Present Global Wind, Solar, and Hydro Power Technologies

Castro

Capellán‐Pérez

2020

Energies

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Whether renewable energy sources (RES) will provide sufficient energy surplus to entirely power complex modern societies is under discussion. We contribute to this debate by estimating the current global average energy return on energy invested (EROI) for the five RES technologies with the highest potential of electricity generation from the comprehensive and internally consistent estimations of their material requirements at three distinct energy system boundaries: standard farm-gate (EROIst), final at consumer point-of-use (EROIfinal), and extended (including indirect investments, EROIext). EROIst levels found fall within the respective literature ranges. Expanding the boundaries closer to the system level, we find that only large hydroelectricity would currently have a high EROIext ~ 6.5:1, while the rest of variable RES would be below 3:1: onshore wind (2.9:1), offshore wind (2.3:1), solar Photovoltaic (PV) (1.8:1), and solar Concentrated Solar Power (CSP) (<1:1). These results indicate that, very likely, the global average EROIext levels of variable RES are currently below those of fossil fuel-fired electricity. It remains unknown if technological improvements will be able to compensate for factors, which will become increasingly important as the variable RES scale-up. Hence, without dynamically accounting for the evolution of the EROI of the system, the viability of sustainable energy systems cannot be ensured, especially for modern societies pursuing continuous economic growth.

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“…The real question is whether the number of jobs created by investing a certain amount of money in SMRs exceeds the number of jobs created by investing the same amount of money in a different but comparable energy technology. Although there is no data on jobs from SMRs-because SMRs have not been deployed at any meaningful level to measure employment figures-the literature is clear that nuclear power generates fewer jobs than renewables like solar and wind energy per unit of energy generated [84], [85]. To the extent that one can make prognoses about the number of jobs that might be created by advanced and small modular nuclear reactors, the outlook would be even more bleak.…”

Section: Will Small Modular and Advanced Nuclear Reactors Be Majmentioning

confidence: 99%

Small Modular and Advanced Nuclear Reactors: A Reality Check

Ramana

2021

IEEE Access

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Nuclear power has been declining in importance over the last quarter century, with its share of global electrical energy generation decreasing from 17.5 percent in 1996 to around 10 percent in 2019. Small modular and advanced nuclear reactors have been proposed as potential ways of dealing with the problemsspecifically economic competitiveness, risk of accidents, link to proliferation and production of wasteconfronting nuclear power technology. This perspective article examines whether these new designs can indeed solve these problems, with a particular focus on the economic challenges. It briefly discusses the technical challenges confronting advanced reactor designs and the many decades it might take for these to be commercialized, if ever. The article explains why the higher construction and operational costs per unit of electricity generation capacity will make electricity from small modular reactors more expensive than electricity from large nuclear power plants, which are themselves not competitive in today's electricity markets. Next, it examines the potential savings from learning and modular construction, and explains why the historical record suggests that these savings will be inadequate to compensate for the economic challenges resulting from the lower generation capacity. It then critically examines arguments offered by advocates of these technologies about job creation and other potential uses of energy generated from these plants to justify subsidizing and constructing these kinds of nuclear plants. It concludes with an assessment of the markets for these technologies, suggesting that these are inadequate to justify constructing the necessary manufacturing facilities.

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Electricity generation technologies: Comparison of materials use, energy return on investment, jobs creation and CO2 emissions reduction

Cited by 46 publications

References 49 publications

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Standard, Point of Use, and Extended Energy Return on Energy Invested (EROI) from Comprehensive Material Requirements of Present Global Wind, Solar, and Hydro Power Technologies

Small Modular and Advanced Nuclear Reactors: A Reality Check

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