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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 18283

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Prof. Dr. Kun Mo Lee

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Guest Editor

Department of Environmental and Safety Engineering, Ajou University,206 Worldcup-ro, Suwon, Gyeonggi-do 16499, Korea
Interests: life cycle assessment; uncertainty analysis; carbon footprint; avoided emissions methods; greenhouse gas emissions

Special Issue Information

Dear Colleagues,

Climate change is envisaged as the single most serious threat to the existence of life on the earth. Greenhouse gas (GHG) emission from human activities such as fossil fuel combustion for obtaining energies is the main cause of climate change. Mitigation and adaptation initiatives are underway; however, the outcomes of those initiatives are not tangible. This may partly be due to inaccurate estimation of the actual GHG emissions from the sources. A famous maxim, “without measurement, no management”, calls for a robust and accurate quantification of GHG emissions for implementing those initiatives. Life cycle assessment (LCA) is the tool of choice for the quantification of GHG emissions.

Claiming “avoided emissions” is common practice in industry sectors including renewable energy, information and communication technologies (ICT), and innovative technologies substituting conventional technologies. Uncertainty around the input and output data for LCA, and life cycle impact assessment results such as GHG emissions, needs to be addressed for proper implementation of LCA. In particular, the uncertainty of emission factors of many types of energies and materials is a main obstacle to the accurate accounting of GHG emissions.

This Special Issue seeks contributions from researchers, industry experts, and academia to the topics addressed above. We therefore invite papers on methodologies, case studies, and reviews, contributing to the advancement of the quantification of GHG emissions and other environmental impacts in the context of LCA.

Prof. Dr. Kun Mo Lee
Guest Editor

Manuscript Submission Information

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Keywords

LCA methodologies related to avoided emissions
Application of the avoided emissions methods to renewable energy, ICT, and innovative technology sectors
Uncertainty analysis methodologies
Application of the uncertainty analysis methods to LCA
Data quality assessment related to the input/output data, including LCI databases
Quantification of GHG emissions of the industrial products and services such as rental, lease, and servicizing
Determination of the GHG emission factors for materials and energies (e.g., electricity, fuels, plastics)

Published Papers (4 papers)

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Research

14 pages, 1250 KiB

Open AccessArticle

Uncertainty of the Electricity Emission Factor Incorporating the Uncertainty of the Fuel Emission Factors

by Kun Mo LEE and Min Hyeok LEE

Energies 2021, 14(18), 5697; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185697 - 10 Sep 2021

Cited by 5 | Viewed by 1788

Abstract

Greenhouse gas (GHG) emission from electricity generation has been recognized as one of the most significant contributors to global warming. The GHG emission factor of electricity (hereafter, electricity emission factor) can be expressed as a function of three different (average, minimum, and maximum) fuel emission factors, monthly fuel consumption, and monthly net power generation. Choosing the average fuel emission factor over the minimum and maximum fuel emission factors is the cause of uncertainty in the electricity emission factor, and thus GHG emissions of the power generation. The uncertainties of GHG emissions are higher than those of the electricity emission factor, indicating that the uncertainty of GHG emission propagates in the GHG emission computation model. The bootstrapped data were generated by applying the bootstrap method to the original data set which consists of a 60-monthly average, and minimum and maximum electricity emission factors. The bootstrapped data were used for computing the mean, confidence interval (CI), and percentage uncertainty (U) of the electricity emission factor. The CI, mean, and U were [0.431, 0.443] kg CO₂-eq/kWh, 0.437 kg CO₂-eq/kwh, and 2.56%, respectively. Full article

(This article belongs to the Special Issue Life Cycle Assessment of Environmental System)

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23 pages, 4292 KiB

Open AccessArticle

Environmental Impact Evaluation of Current Municipal Solid Waste Treatments in India Using Life Cycle Assessment

by Venkata Ravi Sankar Cheela, Michele John, Wahidul K. Biswas and Brajesh Dubey

Energies 2021, 14(11), 3133; https://0-doi-org.brum.beds.ac.uk/10.3390/en14113133 - 27 May 2021

Cited by 20 | Viewed by 4534

Abstract

An environmental life cycle assessment was conducted to compare proposed municipal solid waste treatment systems with the existing system in Visakhapatnam, India. Five waste alternative treatment systems, including open dumping of municipal solid waste (S1), landfill without gas recovery [LFWGR] (S2), landfill with gas recovery (S3), anaerobic digestion + LFWGR (S4), and incineration + LFWGR (S5). EASETECH^TM was considered for assessment using ReCiPE Midpoint (Heuristic) world environmental impact assessment method. Global warming potential (GWP), terrestrial acidification (TA), freshwater eutrophication (FEW), marine water eutrophication (ME), human toxicity (HTP), terrestrial ecotoxicity (TE), freshwater ecotoxicity (FWT), and marine ecotoxicity (MET) impacts were determined for each option. The existing MSW disposal practice in Visakhapatnam city (baseline scenario, S1) has the highest GWP (1107 kg CO₂ eq.), which can potentially be reduced to 68.2%, 81.5%, 98.2%, and 94.5% by alternative waste management scenarios S2, S3, S4 and S5, respectively. Scenario S4, involving the use of anaerobic digestion of food waste and residues dumped in engineered landfill without energy recovery was found to be the option with the highest mitigation potential of most of the impacts, and it contributes to significant environmental benefits in terms of ecological footprints in a low-income country such as India. Sensitivity analysis was conducted to confirm the reasonable legitimacy of data used for the determination of the impacts. Full article

(This article belongs to the Special Issue Life Cycle Assessment of Environmental System)

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26 pages, 2327 KiB

Open AccessArticle

The Trend and Status of Energy Resources and Greenhouse Gas Emissions in the Malaysia Power Generation Mix

by Siti Norasyiqin Abdul Latif, Meng Soon Chiong, Srithar Rajoo, Asako Takada, Yoon-Young Chun, Kiyotaka Tahara and Yasuyuki Ikegami

Energies 2021, 14(8), 2200; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082200 - 15 Apr 2021

Cited by 58 | Viewed by 8384

Abstract

Environmental issues in energy policy, especially global warming, have received more attention lately than ever before. Excessive dependence on fossil fuels, deforestation, and land degradation are the three main factors that lead to increased carbon dioxide (CO

_{2}

) emissions. Consequently, the global [...] Read more.

_{2}

) emissions. Consequently, the global average temperature has doubled compared to anticipation. Various international protocols and agendas have been established, pledged to restore the global average temperature to the 1990 level. As a result, energy policies worldwide have also undergone various transformations to align with these protocols since then. As a developing nation, Malaysian’s electricity demand has continuously grown in the past two decades. To date, the electricity sector is still dominated by fossil fuels. Government incentives have been the most influential factor in the nation’s energy mix trend. Several energy policies implemented throughout the past 22 years have seen the shift from natural gas to coal power in power plants, and in more recent years, renewable energy resources. Numerous studies in the past have independently outlined the status of various energy source in Malaysia. However, they all fell short in providing the greenhouse gas (GHG) emissions in the Malaysian energy sector. Notably, the question that remains to be answered is how GHG emissions have changed in response to the amendment in the energy mix; hence, the effectiveness of policy change in this aspect remains unknown. This paper analysed the past and present trend of Malaysia electricity generation mix and the resultant GHG emissions. In particular, this paper focused on investigating the variation of combined specific GHG emissions in the Malaysian electricity sector, in response to the policy change within the past 22 years. This provides the insight for Malaysian policymakers to evaluate the effectiveness of past policies in GHG emissions and the measures to be taken in future. The finding of this paper shows the attention on the nation’s GHG emissions has evolved over the years, following the diversification in energy mix driven by the policy change. It was also found that, on average, it took a decade for a significant reduction in specific GHG emission to be visible since the government’s energy policy implementation. Full article

(This article belongs to the Special Issue Life Cycle Assessment of Environmental System)

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15 pages, 1600 KiB

Open AccessArticle

Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

by Kun Mo Lee, Min Hyeok Lee, Jong Seok Lee and Joo Young Lee

Energies 2020, 13(18), 4965; https://0-doi-org.brum.beds.ac.uk/10.3390/en13184965 - 22 Sep 2020

Cited by 8 | Viewed by 2832

Abstract

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions. Full article

(This article belongs to the Special Issue Life Cycle Assessment of Environmental System)

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