A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies

Aluminum production is a major energy consumer and important source of greenhouse gas (GHG) emissions globally. Estimation of the energy consumption and GHG emissions caused by aluminum production in China has attracted widespread attention because China produces more than half of the global aluminum. This paper conducted life cycle (LC) energy consumption and GHG emissions analysis of primary and recycled aluminum in China for the year 2020, considering the provincial differences on both the scale of self-generated electricity consumed in primary aluminum production and the generation source of grid electricity. Potentials for energy saving and GHG emissions reductions were also investigated. The results indicate that there are 157,207 MJ of primary fossil energy (PE) consumption and 15,947 kg CO2-eq of GHG emissions per ton of primary aluminum ingot production in China, with the LC GHG emissions as high as 1.5–3.5 times that of developed economies. The LC PE consumption and GHG emissions of recycled aluminum are very low, only 7.5% and 5.3% that of primary aluminum, respectively. Provincial-level results indicate that the LC PE and GHG emissions intensities of primary aluminum in the main production areas are generally higher while those of recycled aluminum are lower in the main production areas. LC PE consumption and GHG emissions can be significantly reduced by decreasing electricity consumption, self-generated electricity management, low-carbon grid electricity development, and industrial relocation. Based on this study, policy suggestions for China’s aluminum industry are proposed. Recycled aluminum industry development, restriction of self-generated electricity, low-carbon electricity utilization, and industrial relocation should be promoted as they are highly helpful for reducing the LC PE consumption and GHG emissions of the aluminum industry. In addition, it is recommended that the central government considers the differences among provinces when designing and implementing policies.

We integrate life cycle indicators for various technologies of an energy system model with high spatiotemporal detail and a focus on Europe and North Africa. Using multi-objective optimization, we calculate a pareto front that allows us to assess the trade-offs between system costs and life cycle greenhouse gas (GHG) emissions of future power systems. Furthermore, we perform environmental ex-post assessments of selected solutions using a broad set of life cycle impact categories. In a system with the least life cycle GHG emissions, the costs would increase by ~63%, thereby reducing life cycle GHG emissions by ~82% compared to the cost-optimal solution. Power systems mitigating a substantial part of life cycle GHG emissions with small increases in system costs show a trend towards a deployment of wind onshore, electricity grid and a decline in photovoltaic plants and Li-ion storage. Further reductions are achieved by the deployment of concentrated solar power, wind offshore and nuclear power but lead to considerably higher costs compared to the cost-optimal solution. Power systems that mitigate life cycle GHG emissions also perform better for most impact categories but have higher ionizing radiation, water use and increased fossil fuel demand driven by nuclear power. This study shows that it is crucial to consider upstream GHG emissions in future assessments, as they represent an inheritable part of total emissions in ambitious energy scenarios that, so far, mainly aim to reduce direct CO2 emissions.

Comparative life cycle greenhouse gas analysis of clean hydrogen pathways: Assessing domestic production and overseas import in South Korea

This manuscript reviews and compares the results of recent greenhouse gas (GHG) emission life-cycle analyses. Specific attention is paid to fossil energy technologies and renewable energy technologies (RETs). It is therefore desirable for GHG emissions under national, regional and international mitigation policies to be accounted for over its entire life-cycle. The results presented here indicate that the most significant GHG avoidance (in absolute terms) can be made from technology substitution. The introduction of advanced fossil fuel technologies can also lead to improvements in life-cycle GHG emissions. Overall, hydro, nuclear, solar and wind energy technologies can produce electricity with the least life-cycle global warming impact. The advantage of using renewable resources for energy production from conventional resources scarcity and power system reliability point of view is too much. Using renewable resources specially wind and solar are becoming common in developing countries for electrifying the remote and rural areas where grid electrification is economically infeasible. This research work shows an investigation that uses a combination of solar and wind energy as hybrid system (HPS) for electrical generation in Dire Dawa. The generated electricity has been utilized for different purposes. The system has also integrated a diesel generator to be more reliable. This system is not linked with conventional energy. The total energy consumption of each household is 29.44 kWh/day. Measurements included the solar radiation intensity, the ambient temperature and the wind speed was collected from national meteorology agency which was collected for 15 years. To simulate the hybrid power system (HPS) HOMER was used. Emissions and renewable energy generation fraction (RF) of total energy consumption are calculated as the main environmental indicator. The net present cost (NPC) and cost of energy (COE) are calculated for economic evaluation. It is found that, for Dire Dawa climates, the optimum results of HPS show a 93.73% reduction of emissions with 99% of renewable energy fraction.

Introducing demand to supply ratio as a new metric for understanding life cycle greenhouse gas (GHG) emissions from rainwater harvesting systems

Impacts of alternative fuel combustion in cement manufacturing: Life cycle greenhouse gas, biogenic carbon, and criteria air contaminant emissions

Life cycle assessment of primary energy demand and greenhouse gas (GHG) emissions of four propylene production pathways in China

Plug-in hybrid electric vehicles (PHEVs) have potential to reduce greenhouse gas (GHG) emissions in the U.S. light-duty vehicle fleet. GHG emissions from PHEVs and other vehicles depend on both vehicle design and driver behavior. We pose a twice-differentiable, factorable mixed-integer nonlinear programming model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation for minimizing lifecycle greenhouse gas (GHG) emissions. The resulting nonconvex optimization problem is solved using a convexification-based branch-and-reduce algorithm, which achieves global solutions. In contrast, a randomized multistart approach with local search algorithms finds global solutions in 59% of trials for the two-vehicle case and 18% of trials for the three-vehicle case. Results indicate that minimum GHG emissions is achieved with a mix of PHEVs sized for around 35 miles of electric travel. Larger battery packs allow longer travel on electric power, but additional battery production and weight result in higher GHG emissions, unless significant grid decarbonization is achieved. PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over ordinary hybrid electric vehicles. Optimal allocation of different vehicles to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statisticalmore » sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.« less

Plug-in hybrid electric vehicle (PHEV) technology has the potential to reduce operating cost, greenhouse gas (GHG) emissions, and petroleum consumption in the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an optimization model integrating vehicle physics simulation, battery degradation data, and U.S. driving data. The model identifies optimal vehicle designs and allocation of vehicles to drivers for minimum net life cycle cost, GHG emissions, and petroleum consumption under a range of scenarios. We compare conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent size and performance (similar to a Toyota Prius) under urban driving conditions. We find that while PHEVs with large battery packs minimize petroleum consumption, a mix of PHEVs with packs sized for ∼25–50 miles of electric travel under the average U.S. grid mix (or ∼35–60 miles under decarbonized grid scenarios) produces the greatest reduction in life cycle GHG emissions. Life cycle cost and GHG emissions are minimized using high battery swing and replacing batteries as needed, rather than designing underutilized capacity into the vehicle with corresponding production, weight, and cost implications. At 2008 average U.S. energy prices, Li-ion battery pack costs must fall below $590/kW h at a 5% discount rate or below $410/kW h at a 10% rate for PHEVs to be cost competitive with HEVs. Carbon allowance prices offer little leverage for improving cost competitiveness of PHEVs. PHEV life cycle costs must fall to within a few percent of HEVs in order to offer a cost-effective approach to GHG reduction.

SummaryFuel economy has been an effective indicator of vehicle greenhouse gas (GHG) emissions for conventional gasoline‐powered vehicles due to the strong relationship between fuel economy and vehicle life cycle emissions. However, fuel economy is not as accurate an indicator of vehicle GHG emissions for plug‐in hybrid (PHEVs) and pure battery electric vehicles (EVs). Current vehicle labeling efforts by the U.S. Environmental Protection Agency (EPA) and Department of Transportation have been focused on providing energy and environmental information to consumers based on U.S. national average data. This article explores the effects of variations in regional grids and regional daily vehicle miles traveled (VMT) on the total vehicle life cycle energy and GHG emissions of electrified vehicles and compare these results with information reported on the label and on the EPA's fuel economy Web site. The model results suggest that only 25% of the life cycle emissions from a representative PHEV are reflected on current vehicle labeling. The results show great variation in total vehicle life cycle emissions due to regional grid differences, including an approximately 100 gram per mile life cycle GHG emissions difference between the lowest and highest electric grid regions and up to a 100% difference between the state‐specific emission values within the same electric grid regions. Unexpectedly, for two regional grids the life cycle GHG emissions were higher in electric mode than in gasoline mode. We recommend that labels include stronger language on their deficiencies and provide ranges for GHG emissions from vehicle charging in regional electricity grids to better inform consumers.

Reducing life cycle greenhouse gas emissions of corn ethanol by integrating biomass to produce heat and power at ethanol plants

The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6%–2.0% reduction in annual life cycle GHG emissions at a cost penalty of 5.1%–6.9% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.

The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of decarbonization apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6-2.0% reduction in annual life cycle GHG emissions at a cost penalty of 3.2–9.6% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.

This paper compares the greenhouse gas (GHG) emissions of natural gas (NG)- based fuels to the GHG emissions of electric vehicles (EVs) powered with NG-to-electricity in China. A life-cycle model is used to account for full fuel cycle and use-phase emissions, as well as vehicle cycle and battery manufacturing. The reduction of life-cycle GHG emissions of EVs charged by electricity generated from NG, without utilizing carbon dioxide capture and storage (CCS) technology can be 36%–47% when compared to gasoline vehicles. The large range change in emissions reduction potential is driven by the different generation technologies that could in the future be used to generate electricity in China. When CCS is employed in power plants, the GHG emission reductions increase to about 71%–73% compared to gasoline vehicles. It is found that compressed NG (CNG) and liquefied NG (LNG) fuels can save about 10% of carbon as compared to gasoline vehicles. However, gas-to-liquid (GTL) fuel made through the Fischer-Tropsch method will likely lead to a life-cycle GHG emissions increase, potentially 3%–15% higher than gasoline, but roughly equal to petroleum-based diesel. When CCS is utilized, the GTL fueled vehicles emit roughly equal GHG emissions to petroleum-based diesel fuel high-efficient hybrid electric vehicle from the life-cycle perspective.