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Life Cycle Environmental Impact of Onshore and Offshore Wind Farms in Texas
Abstract and Figures
The last decade witnessed a quantum increase in wind energy contribution to the U.S. renewable electricity mix. Although the overall environmental impact of wind energy is miniscule in comparison to fossil-fuel energy, the early stages of the wind energy life cycle have potential for a higher environmental impact. This study attempts to quantify the relative contribution of individual stages toward life cycle impacts by conducting a life cycle assessment with SimaPro® and the Impact 2002+ impact assessment method. A comparative analysis of individual stages at three locations, onshore, shallow-water, and deep-water, in Texas and the gulf coast indicates that material extraction/processing would be the dominant stage with an average impact contribution of 72% for onshore, 58% for shallow-water, and 82% for deep-water across the 15 midpoint impact categories. The payback times for CO2 and energy consumption range from 6 to 14 and 6 to 17 months, respectively, with onshore farms having shorter payback times. The greenhouse gas emissions (GHG) were in the range of 5–7 gCO2eq/kWh for the onshore location, 6–9 CO2eq/kWh for the shallow-water location, and 6–8 CO2eq/kWh for the deep-water location. A sensitivity analysis of the material extraction/processing stage to the electricity sourcing stage indicates that replacement of lignite coal with natural gas or wind would lead to marginal improvements in midpoint impact categories.
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... This indicates that system dynamics could affect the energy payback performance. Furthermore, researchers in (Chipindula et al., 2018) conducted life cycle assessments for various configurations of onshore and offshore wind turbines by considering their environmental impacts, carbon, and energy payback periods while estimating their emissions. The proposed configurations include: (1) three onshore turbines with a total rated capacity of 5.3 MW, (2) two shallow water-based offshore turbines with total rated capacity of 4.4 MW, and (3) two deep water-based offshore turbines with total rated capacity of 7.3 MW. ...
... wind turbine design, foundation design, location, distance to shore, depth) [38][39][40][41], 2) how OWFs potentially contribute to a reduction of CO 2− eq. emissions [42][43][44][45][46], or 3) the environmental performance of OWFs vs. onshore wind farms [46][47][48][49]. Few other studies have a different scope, for example Elginoz and Bas [50] conducted an LCA in the context of a multi-use platform to determine the impacts of offshore wind energy combined with wave energy, while Arvensen et al. [51] assessed the impacts of an entire offshore power grid in the North Sea. ...
Renewable offshore wind electricity is as one of the major renewable energy sources on our path towards carbon neutrality. As for all energy technologies, offshore wind farms (OWFs) will have both local and global negative and positive impacts. Understanding and quantifying these burdens and benefits requires a holistic sustainability assessment. This study tests and applies a novel (socio-) environmental impact assessment framework to quantify the monetized (socio-) environmental footprint and handprint of an offshore wind farm located in the Belgian Continental Shelf. This framework consists of a combination of two ways of integrating Life Cycle Assessment (LCA) and Ecosystem Services Assessment (ESA) to quantify both the site-specific and site-generic impacts on ecosystem services (ESs) over the lifetime of a human intervention. For the operation and maintenance stage of the OWF, impacts on three local ESs were quantified, i.e. offshore wind energy provisioning, nursery and habitat maintenance and aesthetic value, while for the other life cycle stages site-generic impacts on multiple ESs were calculated. A comprehensive list of data was inventoried to conduct both the LCA and ESA studies. The monetized impact results were then aggregated and monetized at the level of three areas of protection, i.e. human health and well-being, natural resources and ecosystem quality. The results show that the OWF has a net handprint of +€85,196, mainly due to electricity production, while the absolute footprint (−€4039) consists largely of impacts associated to the supply chain of materials to manufacture the offshore windfarm. Furthermore, this study compares the (socio-) environmental performance of an OWF with nuclear energy, which is used as benchmark because of its high importance for electricity supply in Belgium. This study is a first step towards a valuable contribution to understanding the multi-scale burdens and benefits of offshore wind energy, which can support decision- and policy-making.
... The International Organization for Standardization's (ISO) 14040/44 standard defines LCA as a comprehensive assessment of a product's environmental characteristics. The result of the LCA process is a quantitative measure of the product's environmental friendliness [37][38][39][40][41][42][43][44][45]. In work [34], M. Ruda used an assumption for study the influence of elemental composition of batteries on the sustainability of ecosystems, that would be valid for assessing the impact of wind power plants. ...
The aim of this study is to model the processes of mutual influence of the wind turbine and the environment. An elementary structural element of an ecosystem is a compartment of a complex landscape system in which a wind turbine is considered during its life cycle. The task of creating a ‘cyber-twin’ of a wind turbine, as a technogenic object of the compartment of the complex landscape system, was realized by mathematical modeling of the states of the layers and subsystems of the compartment of the complex landscape system under integrated impact. Impacts are pollutants and harmful factors, primarily of anthropogenic origin, the values of which were estimated by simulation modeling in the experimental part of this study. As a result of mathematical modeling a system of differential equations was obtained, the input data for which were the values of environmental impacts, expressed by the specified indicators. The resulting model will act as ideal for a real system ‘wind turbine-environment’ and will allow predicting the consequences of harmful impact of a wind turbine on a complex landscape system.Keywordsenvironmentlandscape complexrenewable energy sourcesecological indicatorsharmful ecological influencecyber-physical system
... Fig. 4. Contribution of input parameters to environmental impacts for the IFAS-MBR configuration. [41], who studied the impact of renewable energy on ionizing radiation and carcinogenic effects and highlighted the adverse environmental impacts associated with fossil fuel consumption for electricity generation. These findings emphasize the importance of adopting energysaving approaches to minimize the environmental impacts associated with electricity usage [13]. ...
The petrochemical industry is a significant source of hazardous waste, leading to widespread environmental pollution. For the first time, an integrated approach was employed in the current study to combine life cycle assessment (LCA) coupled life cycle cost (LCC) analysis with waste sludge disposal scenarios to evaluate trickling filter (TF), membrane bioreactor-reverse osmosis (MBR-RO), and integrated fixed-film activated sludge-membrane bioreactor (IFAS-MBR) performance at the individual unit level for the treatment of petrochemical wastewater. The midpoint LCA modeling indicated that the main environmental burdens in the studied systems were freshwater eutrophication (<5.31 kg P eq) and toxicity potentials (<263.16 kg 1,4-DCB). These impacts were primarily attributed to the emissions of zinc, copper, nickel, and the processing of heat and gas. Furthermore , the results of the LCC analysis revealed that the TF system was more cost-effective than the MBR-RO and IFAS-MBR systems. By utilizing the produced biogas for energy provision in the TF system, significant reductions in toxicity potentials and ecosystem impact categories ranging from 26.72 % to 56.69 % were observed. Additionally, the biowaste scenario exhibited lower environmental concerns (<50 %) compared to incineration or landfill. In conclusion, this investigation provides valuable perspectives and a sustainable strategy for future policymaking regarding petrochemical wastewater treatment.
... where states for the carbon payback time The GHG payback time of wind turbines in North West of Europe e.g., varies between 1.8-22.5 months, with average period of 5.3 months [44]. In general, it is less than one year for all technologies [45]. ...
Life cycle assessment (LCA) was undertaken for a proposed wind farm of ten Gamesa wind turbines with a 2 MW each. A 20 MW land-based wind turbine’s lifetime primary energy consumption was found
to be 56 GWh, compared to the 2082 GWh of electric energy it produces. Energy payback takes 6.3 months,
has a payback ratio of 38, and an energy intensity of 0.0269 kWhprim/kWhprod. The emission of 8.83 g/kWhprod of CO2 eq. The cost savings associated with CO2 mitigation amount to $155 million in savings. Additionally, the amount of money saved as a result of fuel savings was calculated at $56 485 million, in which
could be used to invest in wind farms development. The GHG avoided emission is 7.7 M ton. The energy consumed by manufacturing process accounted for 79.4%, recycling comes in second with 15.6%, then transportation with 4.6%. The CO2 emissions of production phase of the wind turbine accounted for 63.35% of total
CO2 emissions, while recycling accounted for 33.15% and transportation for 3.5%, with negligible share of
landfilling and operation and maintenance phases.
... where states for the carbon payback time The GHG payback time of wind turbines in North West of Europe e.g., varies between 1.8-22.5 months, with average period of 5.3 months [44]. In general, it is less than one year for all technologies [45]. ...
Life cycle assessment (LCA) was undertaken for a proposed wind farm of ten Gamesa wind turbines with a 2 MW each. A 20 MW land-based wind turbine's lifetime primary energy consumption was found to be 56 GWh, compared to the 2082 GWh of electric energy it produces. Energy payback takes 6.3 months, has a payback ratio of 38, and an energy intensity of 0.0269 kWhprim/kWhprod. The emission of 8.83 g/kWh-prod of CO 2 eq. The cost savings associated with CO 2 mitigation amount to $155 million in savings. Additionally , the amount of money saved as a result of fuel savings was calculated at $56 485 million, in which could be used to invest in wind farms development. The GHG avoided emission is 7.7 M ton. The energy consumed by manufacturing process accounted for 79.4%, recycling comes in second with 15.6%, then transportation with 4.6%. The CO 2 emissions of production phase of the wind turbine accounted for 63.35% of total CO 2 emissions, while recycling accounted for 33.15% and transportation for 3.5%, with negligible share of landfilling and operation and maintenance phases.
Purpose The overall aim of this study is to contribute to the creation of LCA database on electricity generation systems in Ethiopia. This study specifically estimates the environmental impacts associated with wind power systems supplying high voltage electricity to the national grid. The study has regional significance as the Ethiopian electric system is already supplying electricity to Sudan and Djibouti and envisioned to supply to other countries in the region. Materials and methods Three different grid-connected wind power systems consisting of four different models of wind turbines with power rates between 1 and 1.67 MW were analyzed for the situation in Ethiopia. The assessment takes into account all the life cycle stages of the total system, cradle to grave, considering all the processes related to the wind farms: raw material acquisition, manufacturing of main components, transporting to the wind farm, construction, operation and maintenance, and the final dismantling and waste treatment. The study has been developed in line with the main principles of the ISO 14040 and ISO 14044 standard procedures. The analysis is done using SimaPro software 8.0.3.14 multiuser , Ecoinvent database version 3.01, and ReCiPe 2008 impact assessment method. The assumed operational lifetime as a baseline is 20 years. Results and discussion The average midpoint environmental impact of Ethiopian wind power system per kWh electricity generated is for climate change: 33.6 g CO 2 eq., fossil depletion: 8 g oil eq., freshwater ecotoxicity: 0.023 g 1,4-DCB eq., freshwater eutrophication: 0.005 g N eq., human toxicity: 9.9 g 1,4-DCB eq., metal depletion: 18.7 g Fe eq., marine eco-toxicity: 0.098 g 1,4-DCB eq., particulate matter formation: 0.097 g PM10 eq., photochemical oxidant formation: 0.144 g NMVOC, and terrestrial acidification: 0.21 g SO 2 eq. The pre-operation phase that includes the upstream life cycle stage is the largest contributor to all the environmental impacts, with shares ranging between 82 and 96%. The values of cumulative energy demand (CED) and energy return on investment (EROI) for the wind power system are 0.393 MJ and 9.2, respectively. Conclusion The pre-operation phase is the largest contributor to all the environmental impact categories. The sensitivity and scenario analyzes indicate that changes in wind turbine lifespans, capacity factors, exchange rates for parts, transport routes, and treatment activities would result in significant changes in the LCA results.
With single components weighing up to hundreds of tonnes and lifted to heights of approximately 100 m, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. Guidelines and standards for health and safety in lifting operations exist; however, having people directly beneath the load is still common practice in offshore wind turbine installations. Concepts for human-free offshore lifting operations in the categories of guidance and control, connections, and assembly are studied in this work. This paper documents the process of applying Multi-Criteria Decision Analysis (MCDA), using experts’ opinions for the importance of defined criteria obtained by conducting an industry survey, to benchmark the suitability of the concepts at two stages. Stage one streamlined possible options and stage two ranked the remaining suite of options after further development. The survey results showed that criteria such as ‘reduction of risk’, ‘handling improvement’ and ‘reliability of operation’ were most important. The most viable options, weighted by industry opinion, to remove personnel from areas of high risk are: Boom Lock and tag lines, a camera system with mechanical guidance, and automated bolt installation/fastening for seafastening. The decision analysis framework developed can be applied to similar problems to inform choices subject to multiple criteria.
This study characterized and evaluated the life cycle greenhouse gas (GHG) emissions from different wind electricity generation systems by (a) performing a comprehensive review of the wind electricity generation system life cycle assessment (LCA) studies and (b) statistically evaluating the life cycle GHG emissions (expressed in grams of carbon dioxide equivalent per kilowatt hour, gCO2e/kWh). A categorization index (with unique category codes, formatted as ‘axis of rotation-installed location-power generation capacity’) was adopted for use in this study to characterize the reviewed wind electricity generation systems. The unique category codes were labeled by integrating the names from the three wind power sub-classifications, i.e., the axis of rotation of the wind turbine [horizontal axis wind turbine (HAWT), vertical axis wind turbine (VAWT)], the location of the installation [onshore (ON), offshore (OFF)], and the electricity production capacity [small (S), intermediate (I), large (L)]. The characterized wind electricity generation systems were statistically evaluated to assess the reduction in life cycle GHG emissions. A total of five unique categorization codes (HAWT-ON-S, HAWT-ON-I, HAWT-ON-L, HAWT-OFF-L, VAWT-ON-S) were designated to the 29 wind electricity generation LCA studies (representing 74 wind system cases) using the proposed categorization index. The mean life cycle GHG emissions resulting from the use of HAWT-ON-S (N = 3), HAWT-ON-I (N = 4), HAWT-ON-L (N = 58), HAWT-OFF-L (N = 8), and VAWT-ON-S (N = 1) wind electricity generation systems are 38.67, 11.75, 15.98, 12.9, and 46.4 gCO2e/kWh, respectively. The HAWT-ON-I wind electricity generation systems produced the minimum life cycle GHGs than other wind electricity generation systems.
The purpose of this study is to evaluate various means of wind power turbines installation in the Korean west–south wind farm (Test bed 100 MW, Demonstrate site 400 MW). We presented the marine environment of the southwest offshore wind farm in order to decide the appropriate installation vessel to be used in this site. The various vessels would be WTIV (Wind turbine installation vessel), jack-up barge, or floating crane … etc. We analyzed the installation cost of offshore wind turbine and the transportation duration for each vessel. The analysis results showed the most suitable installation means for offshore wind turbine in the Korean west–south wind farm.
In order to facilitate increased renewable energy production, there continues to be a global increase in wind turbine installation. When quantifying the carbon offsets from these installations, the production emissions are rarely accounted for. This paper reports on the embodied carbon emissions from the production of 14 wind turbines, rated between 50 kW and 3.4 MW. The embodied emissions were quantified from emission factors specific to each material involved in manufacture, transport to site, and installation of the turbines. The resulting trend was that higher-rated turbines had greater embodied carbon emissions with one 3 MW turbine incorporating 1046 tCO2eq compared to only 58 tCO2eq for an 80 kW turbine. However, the greater electricity output of the turbines offset these emissions more quickly with a recovery in 64 days for a 3.4 MW turbine compared to 354 days for a 100 kW one. This also resulted in lower carbon emissions per kilowatt hour of electricity generated and quicker payback as a percentage of lifetime of 0.9% for a 3.4 MW turbine compared to 4.3% and 4.9% for a 50 and 100 kW turbines, respectively. The findings of this analysis indicate that a preference for installation of higher-rated, over lower-rated, turbines should be favoured for greater environmental benefits.
Load factors, defined as portion of utilized engine power, are used in estimation of the diesel mining equipment fuel consumption. Every type of equipment is involved in the specific work operation, common in quarrying of crushed stone. Furthermore, load factors are specific for the equipment type and their application/operating conditions. Based on the mining company’s empirical data on fuel consumption, load factors of the main equipment in quarrying of crushed stone are determined in this paper. This includes bulldozer, backhoe excavators, wheel loaders, trucks, blasthole drill, mobile crushing and screening plants, and mobile belt conveyor. With an assumption of similar operating conditions, those factors can be considered as characteristic for small quarries of crushed stone, but also for mining on other surface pits, depending on the specific equipment application. The obtained load factors are compared to the available data from other sources in order to verify the results and establish the appropriate procedure for assessment ofunknown load factors in different operating conditions.
Wind-power development in the U.S. occurs primarily on private land, producing royalties for landowners through private contracts with wind-farm operators. Texas, the U.S. leader in wind-power production with well-documented support for wind power, has virtually all of its ~12 GW of wind capacity sited on private lands. Determining the spatial distribution of royalty payments from wind energy is a crucial first step to understanding how renewable power may alter land-based livelihoods of some landowners, and, as a result, possibly encourage land-use changes. We located ~1700 wind turbines (~2.7 GW) on 241 landholdings in Nolan and Taylor counties, Texas, a major wind-development region. We estimated total royalties to be ~$11.5 million per year, with mean annual royalty received per landowner per year of $47,879 but with significant differences among quintiles and between two sub-regions. Unequal distribution of royalties results from land-tenure patterns established before wind-power development because of a “property advantage,” defined as the pre-existing land-tenure patterns that benefit the fraction of rural landowners who receive wind turbines. A “royalty paradox” describes the observation that royalties flow to a small fraction of landowners even though support for wind power exceeds 70 percent.
Problem statement: Bulldozers consume a large amount of diesel fuel and consequently produce a significant quantity of CO 2. Environmental and economic cost issues related to fuel consumption and CO 2 emission represent a substantial challenge to the mining industry. Approach: Impact of engine load conditions on fuel consumption and the subsequent CO 2 emission and cost was analyzed for Caterpillar bulldozers. Results were compared with the data on bulldozers' fuel consumption from an operating coal surface mine in the United States. Results: There is a strong linear correlation among power, fuel consumption and engine load factor. Reduction in load factor by 15% may significantly reduce the fuel consumption and the CO 2 emission. Conclusion/Recommendation: Application of appropriate bulldozer's load factor may help mine operators manage fuel consumption, cost and environmental burden.
Installation of floating wind turbines is a challenging task. The time and costs are closely related to the installation method chosen. This paper investigates the performance of an efficient installation concept – a catamaran wind turbine installation vessel. The vessel carries pre-assembled wind turbine units including towers and rotor nacelle assemblies. Each unit is placed onto a pre-installed offshore support structure (in this paper a spar floater) during installation. The challenge is to analyse the responses of the multibody system (catamaran-spar-wind turbine) under simultaneous wind and wave loads. Time-domain simulations were conducted for the coupled catamaran-spar system with mechanical coupling, passive mooring system for the spar, and dynamic positioning control for the catamaran. We focus on the steady-state stage prior to the mating process between one turbine unit and the spar, and discuss the effects of wind loads and wave conditions on motion responses of the catamaran and the spar, relative motions at the mating point, gripper forces and mooring forces. The relative motion at the mating point is less sensitive to the blade orientation, but influenced by the wave conditions. Under the investigated sea states, the present installation method shows decent performance.
This study attempted to evaluate the environmental impact and energy benefit of offshore wind power systems using life cycle assessment (LCA) and net energy analysis. The environmental impact of offshore wind power systems is based primarily on ferrous metal, which is used to install the foundations, towers, and nacelles. The impact categories with the greatest relevance were fossil fuels and respiratory inorganics. This study assumed that the life cycle of an offshore wind power system has four stages (production, installation, operation and maintenance, and end-of-life). Two scenarios were examined in this study. The major difference between the scenarios was that Scenario 2 included an offshore substation. The overall environmental impact in Scenario 2 was higher than that in Scenario 1 by approximately 10%. The net energy analysis in this study included the evaluations of cumulative energy demand (CED), energy return on investment (EROI), and energy payback time (EPT). For Scenarios 1 and 2, CED was 0.192 and 0.216 MJ/kWh, EROI was 18.7 and 16.7, and EPT was 12.8 and 14.4 months, respectively. Moreover, when the recycling of waste materials was considered, each scenario produced a 25% lower environmental impact, 30% lower energy requirement, and 4 months lower EPT.
This study aims to assess the environmental impacts related to the provision of 1 kWh to the grid from wind power in Europe and to suggest how life cycle assessment can inform technology development and system planning. Four representative power plants onshore (with 2.3 and 3.2 MW turbines) and offshore (4.0 and 6.0 MW turbines) with 2015 state-of-the-art technology data provided by Siemens Wind Power were assessed. The energy payback time was found to be less than 1 year for all technologies. The emissions of greenhouse gases amounted to less than 7 g CO2-eq/kWh for onshore and 11 g CO2-eq/kWh for offshore. Climate change impacts were found to be a good indicator for overall hotspot identification however attention should also be drawn to human toxicity and impacts from respiratory inorganics. The overall higher impact of offshore plants, compared to onshore ones, is mainly due to larger high-impact material requirements for capital infrastructure. In both markets the bigger turbines with more advanced direct drive generator technology is shown to perform better than the smaller geared ones. Capital infrastructure is the most impactful life cycle stage across impacts. It accounts for more than 79% and 70% of climate change impacts onshore and offshore respectively. The end-of-life treatment could lead to significant savings due to recycling, ca. 20–30% for climate change. In the manufacturing stage the impacts due to operations at the case company do not exceed 1% of the total life cycle impacts. This finding highlights the shared responsibility across multiple stakeholders and calls for collaborative efforts for comprehensive environmental management across organizations in the value chain. Real life examples are given in order to showcase how LCA results can inform decisions, e.g. for concept and product development and supply chain management. On a systems level the results can be used by energy planners when comparing with alternative energy sources.