An assessment of harvesting practices of sugarcane in the central region of Thailand

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.

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

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

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.

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

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.

Life cycle greenhouse gas emissions and freshwater consumption of liquefied Marcellus shale gas used for international power generation

Previous studies have confirmed the agricultural stage as an important contributor to total tea life cycle greenhouse gas (GHG) emissions. We therefore focus on the growing and processing of black tea and evaluate the GHG reduction potential of possible agricultural management interventions for tea produced from Unilever’s tea estates and factories in Kenya. A baseline GHG footprint was calculated using data collected over four years. Potential interventions to reduce the GHG footprint per kg of black tea focused on key drivers (yield and fertiliser use) of the GHG footprint. These interventions and their effects on production are evidence-based but hypothetical and relate to possible changes in: (1) Harvesting practices; (2) Fertiliser application (timing) and (3) Organic production. Improvements in fertiliser application and harvesting practices could potentially deliver 16% and 9% reduction in GHG emissions, respectively. Under favourable conditions (sourcing fertiliser locally and no yield decline), changing to organic production could theoretically deliver 25% reduction in GHG emissions. However, under less favourable conditions (European sourced fertiliser and 30% yield decline), GHG emissions could increase by 41%. The availability of locally sourced organic fertiliser, sufficient to maintain nitrogen application rates, is seen as the biggest challenge in delivering sustainable organic tea cultivation.

Improving energy and GHG performance of the rice-wheat rotation system: A life-cycle analysis based on a large-scale behavior investigation

We suggest a 2D-plot representation combined with life cycle greenhouse gas (GHG) emissions and life cycle cost for various energy conversion technologies. In general, life cycle assessment (LCA) not only analyzes at the use phase of a specific technology, but also covers widely related processes of before and after its use. We use life cycle GHG emissions and life cycle cost (LCC) to compare the energy conversion process for eight resources such as coal, natural gas, nuclear power, hydro power, geothermal power, wind power, solar thermal power, and solar photovoltaic (PV) power based on the reported LCA and LCC data. Among the eight sources, solar PV and nuclear power exhibit the highest and the lowest LCCs, respectively. On the other hand, coal and wind power locate the highest and the lowest life cycle GHG emissions. In addition, we used the 2D plot to show the life cycle performance of GHG emissions and LCCs simultaneously and realized a correlation that life cycle GHG emission is largely inversely proportional to the corresponding LCCs. It means that an expensive energy source with high LCC tends to have low life cycle GHG emissions, or is environmental friendly. For future study, we will measure the technological maturity of the energy sources to determine the direction of the specific technology development based on the 2D plot of LCCs versus life cycle GHG emissions.

Life cycle greenhouse gas (GHG) emissions from the production of nuclear power (in g CO2e/kWh) are uncertain due partly to a paucity of data on emissions from individual phases of the nuclear fuel cycle. Here, we present the first comprehensive life cycle assessment of GHG emissions produced from the mining and milling of uranium in Canada. The study includes data from 2006-2013 for two uranium mine-mill operations in northern Saskatchewan (SK) and data from 1995-2010 for a third SK mine-mill operation. The mine-mill operations were determined to have GHG emissions intensities of 81, 64, and 34 kg CO2e/kg U3O8 at average ore grades of 0.74%, 1.54%, and 4.53% U3O8, respectively. The production-weighted average GHG emission intensity is 42 kg CO2e/kg U3O8 at an average ore grade of 3.81% U3O8. The production-weighted average GHG emission intensity drops to 24 kg CO2e/kg U3O8 when the local hydroelectric GHG emission factor (7.2 g CO2e/kWh) is substituted for the SK grid-average electricity GHG emission factor (768 g CO2e/kWh). This results in Canadian uranium mining-milling contributing only 1.1 g CO2e/kWh to total life cycle GHG emissions from the nuclear fuel cycle (0.7 g CO2e/kWh using the local hydroelectric emission factor).

Impacts of pre-treatment technologies and co-products on greenhouse gas emissions and energy use of lignocellulosic ethanol production

Assessing the effectiveness of building retrofits in reducing GHG emissions: A Canadian school case study

We pose a reformulated model for optimal design and allocation of conventional (CV), hybrid electric (HEV), and plug-in hybrid electric (PHEV) vehicles to obtain global solutions that minimize life cycle greenhouse gas (GHG) emissions of the fleet. The reformulation is a twice-differentiable, factorable, nonconvex mixed-integer nonlinear programming (MINLP) model that can be solved globally using a convexification-based branch-and-reduce algorithm. We compare results to a randomized multistart local-search approach for the original formulation and find that local-search algorithms locate global solutions in 59% of trials for the two-segment case and 18% of trials for the three-segment case. The results indicate that minimum GHG emissions are achieved with a mix of PHEVs sized for 25–45 miles of electric travel. Larger battery packs allow longer travel on electrical energy, but production and weight of underutilized batteries result in higher GHG emissions. Under the current average U.S. grid mix, PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over HEVs when driven on the standard urban driving cycle. Optimal allocation of different PHEVs to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.