Elevated fuel utilization potential -
Understanding the drivers of greenhouse gas emissions in food production systems is becoming urgent. For wild capture fisheries, fuel use during the fishing phase generally dominates emissions and is highly variable between fisheries.
Fuel use is also essential for the economy of the fisheries, but fuel-intensive fisheries can still be profitable due to fuel subsidies, in particular, if the target species is of high value. Developing an innovative bottom-up approach based on detailed catch and spatial fishing effort data, in the absence of direct fuel data, we analysed the fuel use intensity fuel use per kg landed and economic efficiency landing value per litre fuel used of Danish capture fisheries for the period An overall decline in fishing effort did not significantly affect the overall fuel use intensity and efficiency, which was stable for most of the fleet segments and marine species.
Robust differences in fuel use intensity among individual fisheries, reflected differential spatial accessibility and vulnerability of target species to fishing.
In addition, different fishing techniques targeting the same set of species showed differences in fuel use per unit landed. Danish seining and gillnets had a lower fuel use intensity and higher economic efficiency than demersal trawling; and purse seining than pelagic trawling.
The variability between stocks and fleets also indicates that there is generally potential for improvement in overall efficiency from improved stock status. Short-term management actions to promote the best available fuel-efficient fishing techniques combined with additional long-term actions to secure the recovery of stocks have the potential to reduce fishery greenhouse gas emissions.
Sustainable fisheries and normative environmental management are crucial to developing incentives towards reducing fuel use whenever the fishing sector industry and science work jointly at implementing solutions, as incentives for the industry to reduce fuel use are limited as long as the fishing activity is profitable.
A global challenge for capture fisheries is to promote fishing practices that minimize their environmental impacts while maximizing the societal value generated Pikitch et al. One major impact from fisheries is generally high levels of fuel use leading to greenhouse gas emissions, especially given that fuel is in many cases subsidised depending on country-specific fuel tax policy EP, ; Ziegler and Hornborg, ; Sumaila et al.
Since fuel use is also an essential aspect of the fishing economy, finding less fuel-intensive fishing practices for seafood production has been an issue for several decades FAO, Extensive efforts have therefore been made to find innovative technical solutions for saving fuel, including modernization and renewal of the fishing fleet, investments in energy-efficient propellers, gears and other equipment, replacement of engines, and the construction of new energy-efficient vessel hulls and other energy-efficient procedures for fishing activities e.
However, energy use is now further challenging the fishing capture sector because of the need to end the use of fossil fuels and the episodic rise in fuel prices, which will likely rise again over the coming years e.
Furthermore, the influence of fisheries management on fuel use has received increased attention, such as the importance of gear-technical regulations, quota setting and other management actions Tyedmers et al.
The overarching aim of the Common Fisheries Policy CFP , EU of the European Union EU is to ensure that fishing is environmentally, economically and socially sustainable.
The goal is to optimize economic activity and societal value art. Therefore, policymakers and managers in EU Member states should provide incentives to fishing vessels deploying selective fishing gear or using fishing techniques with reduced environmental impact, such as reduced energy consumption and habitat damage Figure 1.
However, operationalizing these policy ambitions is a complex task. For example, fishing practices performing equally from an economic perspective may have different ecosystem impacts e. Furthermore, although lower carbon footprint aligns with policy ambitions to minimize the effects on marine ecosystems ambitions described in EC, art.
However, if reducing fuel use has drawn increasing attention, it is still uncertain how and to what extent the resulting economic income from catches could be decoupled from this reduction to at least maintain the same profitability e.
Any information about how this decoupling could be done is attractive to policymakers who want to identify efficient actions to meet environmental targets. Figure 1 An idealized virtuous circle showing incremental steps that would provide incentives to fishing vessels deploying selective fishing gear or using fishing techniques with reduced environmental impact toward maintaining healthy and productive marine ecosystems supporting the fisheries and their future yields.
What drives the fuel and economic efficiency of fisheries needs to be better understood by collating and analysing more data Schau et al. Identifying the most efficient production methods between different fishing techniques and management systems may ultimately promote environmentally friendly fishing practices.
Ideally, data on fuel use would be collected and be readily available for all fisheries for this type of analysis. However, high-resolution data on fuel use in fisheries are very sparse and almost never publicly available, which is why different approaches of estimating fuel use indirectly have been developed.
Engine power and fishing effort are two central parameters determining fuel use and have previously been used to estimate the fuel use of fisheries with reasonable accuracy, which has been shown through validation with actual fuel use data Tyedmers et al.
If a relationship between landings per unit effort, stock size, fishing mortality and fuel use could be empirically established, it may be used to predict future fuel use intensity FUI, defined as litres of fuel consumed per kg landed and the potential for decarbonisation of fisheries here from lowering the emissions linked to fuel used during the fishing operations under different fisheries or environmental management scenarios.
Saving fuel and reducing emissions would also likely go hand-to-hand with higher economic returns and economic efficiency defined here as landing value per litre fuel used. In this study, we have updated the methodology to estimate fuel use from fishing effort data developed in Bastardie et al.
We used the estimates to identifiy and discuss the potential for improving existing or future technical innovation-oriented approaches developed to minimize fuel use and potential leverages to reduce greenhouse gas emissions of the Danish fishing sector studied. Fisheries management should integrate minimizing fuel use as a goal due to its importance for climate and fisheries economy.
In this study, we found i that fuel use intensity and economic efficiency did not show specific trends over a year period and across all segments of the Danish fishing fleet, ii that similar fishing efforts by different fleet segments showed differences in catch composition and volume that explain the relative difference in fuel use intensity, iii that fuel use intensity and economic efficiency generally improved with stocks closer to management targets, and iv that fuel use intensity reductions may be achieved by promoting a combination of technical solutions to reduce fuel consumption and management actions to improve stock status.
Finally, we discuss the implications of alternatives to trade-offs between rebuilding stocks and ensuring that fishing businesses remain financially viable when implementing the uptake of energy-efficient fishing practices over time may also improve the health of exploited fish stocks.
The analysis splits the overall fishing effort of Danish vessels into fisheries based on the type of species being fished, area fished ICES Subareas dk , fishing gear type and gear mesh size used. The analysis distinguishes between the large and small vessels as they differ in the data source available see below.
The investigation further distinguishes the large from small gear meshes used by those vessels. The gear mesh variable is a solid explanatory factor for the target assemblage of species being caught by the diverse fleet segments. Finally, the analysis distinguishes the bottom contacting gears from the pelagic gears, as the difference in fuel use and fuel use intensity is found to be quite extensive.
This fleet segmentation provides consistent entities on which EU fisheries management and technical measures typically apply EU, , i. For example, in the DCF the fleet segment For the sole year, Danish vessels out of conducting bottom trawling were covered, representing out of the trips realized in this category.
Also, 51 vessels conducting pelagic fisheries out of 60 in logbooks were treated, representing trips out of trips. Smaller non-VMS-equipped vessels i.
Hence, the coverage of the Danish fishing fleet was excellent as all the Danish vessels having logbooks i. A time series — of data was constructed on effort, catch volume and value based on the economic data collected under the EU data collection framework for European fishing fleets EC, and reported in fishing vessels logbooks we merged with sales slips.
Trip-based data included departure hour date and arrival hour date of each fishing trip, landed amount in kg per species, visited area i. This enabled estimates of the trip duration in hours for each trip of each recorded vessel, which data, combined with sales slips, also identify the origin of the retained catch for each gear in volume and value per area.
The use of VMS or AIS, when available, is an add-on to identify where and when the effective effort takes place i. VMS equipped vessels conducting bottom fishing TBB, OTT, OTB, SSC, SDN, PTB, DRB were treated identically with vessels operating pelagic fishing PTM, OTM, PS , apart from the fact that no seafloor area swept is calculated for pelagic fishing.
For vessels equipped with VMS, fuel consumption per hour was based on the recorded engine power kW in logbooks as the primary explanatory factor for a maximum fuel consumption rate factor fmax , in litre fuel per hour following Bastardie et al.
Other factors are known to strongly influence the actual fuel use, such as the speed of the vessel e. Therefore, the speed of vessel s recorded in VMS data was used to decrease the theoretical maximal fuel consumption f max during the steaming phase by a cubic factor see Ronen, , i.
This corrective decrease in fuel consumption rate along with lower speed was, however, not applied during the fishing phases of vessels using towed gear.
As a rule of thumb, trawl designers are used to scaling the size of trawls to the performance of the engine, which has been confirmed by examining flow meter data of individual trips using trawl Ole Eigaard, pers. Finally, using spatial VMS data linked to the official logbook declaration allows for an estimated fuel use and catch volume in a given location on a trip basis for each vessel.
For small vessels without VMS data but with departure and end dates in hours for each trip recorded in logbooks vessels larger than 8 m in the Baltic Sea and 10 m in the North Sea , automatic identifier system AIS data was used for sampled vessels.
The AIS was used for safety reasons on board any type of vessel at sea but was not mandatory and therefore does not cover the entire fishing vessel fleet.
However, some vessels, including small vessels, carry the device on board. AIS is a system recording the positioning of the vessel every second and hence provides fine tracks of fishing vessel movement at sea. Using the AIS it is possible to deduce the speed profile for each sampled vessel knowing the distance traveled every second during the individual trips of that vessel.
Aggregate speed profiles makes it possible to obtain an average speed profile per fleet segment per trip and calculate the fuel consumption. The typical fuel consumption per hour was computed from the vessel kW as described above for the large VMS-equipped vessels , and typical activity-specific speed profile.
Total consumption were deduced per vessel for each trip within the departure and arrival hour interval. The average estimates at the fleet segment level a certain number of vessels consuming a combined amount of fuel result from aggregating individual vessel fuel use estimates and vessel catch data, eventually expressing the average by species in volume from the declaration in logbooks and in monetary value from the sales slip.
For these segments, estimates were provided for fuel use intensity FUI, fuel used per kg landed , catch-fuel efficiency CPUF, kg catch landed per litre fuel consumed during the entire trip steaming and fishing phases , and economic efficiency VPUF, landing value EUR per unit of fuel , the latter as uptake in cash profit for the fisheries segments.
Finally, a scoring of Danish segments is presented based on FUI estimates. The scoring combines each fleet segment of the average litre of consumed fuel per EUR catch with the average litre per kg catch FUI , after conversion of the two columns to percentages across fleet segments.
The scoring, further standardized to 0 to 5, assumes an equal weighting between litre per euro catch and litre per kilo catch. For example, some fisheries could score low i.
more efficient toward 0 because it would provide a high value per unit of fuel, but possibly with low kg per unit of fuel. However, better scores are expected for fleet segments that simultaneously offer high monetary value and high kg per unit of fuel.
We used our catch-fuel efficiency CPUF estimates in order to evaluate a possible correlation between fuel use and change in stock size of the targeted species over the period. dk , comprising the most recent published assessments, conducted up to and including This function uses approved time-series analyses such as the ARIMA model in R e.
In our case, the analyses are useful to account for possible time lag effects to detect possibly misleading correlations between high effort and low F during the period of increasing abundances, and low effort with high F in periods of declining abundance.
Another limitation in finding out meaningful correlations we cannot exclude would result from the fact that F MSY may be achieved before stock biomasses are entirely rebuilt.
The analysis here aggregates individual fishing trips to provide estimates of fuel use for a number of selected fisheries and species. This breakdown offers different opportunities than earlier studies e. Yet, this breakdown of the overall fuel use per fished species might sometimes be misleading in mixed fisheries whenever several different species caught simultaneously are only marginally contributing to the catch volume for the same fuel input.
One limitation of our study is its reliance on theoretical model of fuel use instead of direct data based on field measurements of consumed fuel. These data will be remain unavailable as long as a monitoring programme collecting accurate data on fuel consumption at the vessel level will be lacking in EU.
Although our estimation approach might bias in the estimates in absolute numbers, the relative change in fuel comsumption should be more robust. However, applying the same fuel use per h models depending solely on vessel speeds will always be prone to bias caused by additional aspects known to influence fuel use and catch efficiency, such as weather conditions, skipper and crew experience Ruttan and Tyedmers, and general technological development and subsidized fleet renewals Eigaard, ; Eigaard et al.
Additionally, if engine power is underreported in the fleet register, the fuel consumption rate would be underestimated EC, This problem is, however, likely not there for the Danish fleet case as no cases of infringement have been found EC, Besides, we have only looked at the fuel consumption of the main engine while auxiliary engine may be embarked onboard vessels and used to supply electricity and hydraulic power onboard, which is also likely to depend on the vessel size and type of fishery Basurko et al.
In cases where both types of high-quality data have been available e. However, it is now possible to compare the results of the large versus the small vessels from this study directly due to the standardisation of the input data coupled robust VMS-logbooks data compared to logbooks-partial AIS data for small vessels , even if there are still caveats analysing fuel use intensity for small vessels using passive gears with a kW-based approach because the fishing patterns hours steaming or fished , and areas for fishing zones are unknown, and assumptions have to be made that are difficult to check e.
Declared catches in logbooks do not include the entire removals that deplete the fished stocks, as so-called discard of targeted species with a TAC occur during the operation of the fishing. Discarding was still allowed in Europe up to 1 st of January when the Landing Obligation came into force.
In our study, no attempt were made to raise the catch to include the non-retained and non-recorded part. The comparison of relative fuel use intensity and the link to stock status is therefore uncertain in that respect.
Even though identifying correlations between efficiencies and stock status is a first step, further investigations are needed due to the short length of the time series that might affect the present cross-correlation analysis.
However, the hypothesis that the time series reflects a stationary process of the underlying biological dynamics is likely to be increasingly violated with a more extended time series, due for example to frequent technical changes in gear selectivity, advocating for restricting the analysis to a short time window of data.
With a higher number of stocks assessed, the signal captured by the cross-correlation analysis will likely be improved, even if specifics to some stocks as shown here will remain.
The more fundamental problem is that we thus far only have looked at correlations, not causation. Therefore, with longer time series, it would be worthwhile to apply a more robust method e. After the currently monitored recovery phase STECF, a , it is likely that for some fisheries, the link between fuel intensity and stock size becomes stronger, with catch rates increasing along with an increase in stock abundance resulting in a reduced fuel intensity.
In contrast, even after recovery, other fisheries may still experience a flat relationship or show an inverse relationship, so that a period of greater fishing effort corresponds to a period of lower fishing mortality in the historical time series if the stock is high and vice versa if the stock is low e.
Depending on the fleet adaptation and the stock level and density effects, this means that reductions in fishing effort may not translate immediately into equivalent reductions in fishing mortality and thus not in reducing the fuel use intensity.
Besides this, if reduction in fishing effort would translate into saving fuel costs, it is uncertain how better stock development might increase the economic efficiency. There are other economic aspects that come into play in the make up of the income from landings and profit, especially the fish prices and fish price changes over time or among marketable categories, which is only partially touched upon in our evaluation.
For example, even under the exact same catch limit, if any, it is be expected that the economic efficiency increase along with larger revenue from stocks in better shape just because large more priced individuals would be caught.
The overall fuel use in Danish fisheries showed interannual variability without a general trend, resulting in an overall net increased fuel consumption per active vessel for a unit of effort hour Figure 2.
This excepts the fuel consumption for large pelagic vessels, which increased drastically in the three most recent years , while effort concomitantly dropped. Therefore, the underlying fishing effort deployed by the Danish fleet at sea continuously declined over the period , both measured in hours at sea and the number of active vessels Figure 2.
A sharp decline in fishing effort was observed in for m vessels conducting pelagic fisheries, likely arising from a restructuring of this fleet Dinesen et al. Indeed, this decline in nominal effort over the 15 years is concomitant with implementing an ITQ system since , Dinesen et al.
Additionally, the total income from landings has been constant for vessels using bottom-contacting gears, while increasing for pelagic vessels, this latter at the highest level in the more recent years Figure 2. Although the effort decreases in hours fished, the fuel consumption does not decrease accordingly because the remaining vessels were larger or have larger engines, which shows that effort deployed in hours was definitely not a sufficient proxy for fuel use intensity.
Figure 2 Time development for large VMS-equipped Danish vessels using bottom contact gears top panel or pelagic gears bottom panel of effective fishing effort A, E , number of active vessels B, F , fuel use C, G , and landed value D, H during the period Effective effort is deduced from the VMS positioning analysis coupled to logbooks.
Fuel use intensity estimates were found to be stable and consistent over the period for most segments because differences among segments were more extensive than changes over time. General patterns in the data showed that some vessels, regardless of size, use the same combination of gears, mesh sizes, and area visited, which leads to catches of similar assemblages of species.
Larger vessels use mainly active gear such as trawls, seines and dredges that were dragged through the water, either on the seafloor with bottom-contacting gear or in the water column chasing for pelagic fish, whereas smaller vessels also deploy passive gear such as nets, hook and line fishing, and pots and traps depending on the species targeted.
Therefore, the study outcomes were described below with regard to this broader fleet segmentation. Fisheries using bottom-contacting gear in Danish waters and beyond are large and spatially diverse Figure 3.
Among them, bottom-contacting gear with small mesh sizes were the main catch methods for Danish fisheries Supplementary Material Figure 2. The largest fuel consumption in overall volume consumed has occurred in the southern North Sea, catching high volumes of small fish per unit effort, such as sandeel Ammodytes spp.
and European sprat Sprattus sprattus , or Norway pout Trisopterus esmarkii close to the Shetland Islands in the northern North Sea Figure 4. In Denmark, sprat is fished by demersal and pelagic trawls.
The fishery for sandeel made up a large share of total landings, landings value and fuel used of the whole Danish fleet over the period studied Supp. These North Sea forage fish fleet segments demonstrated a low fuel use intensity 0.
Bottom trawling for sandeel showed large fluctuations in catch volume over the years. Each of these segments is specialized, landing only a few species Figure 5. Other species, such as hake Merluccius merluccius , horse mackerel Trachurus trachurus and haddock Melanogrammu s aeglefinus , are marine species bycaught by these fleets.
For most fleet segments, the fuel use intensity was stable over , and it increased dramatically in for the larger vessels active in the North Sea with the otter bottom trawl gear Supplementary Material Figure Figure 4 Average of indicators for bottom-contacting gears deployed during by Danish fleets in FAO Icelandic Waters, or EU Western Waters are not shown aggregated on 1x1 minute grid cells.
The larger grid corresponds to the FAO areas delineations. Figure 5 Landings kg per litre of fuel consumed CPUF per fleet-segment across species for large vessel using bottom contacting gears toward demersal species, or toward small forage fish, or for large vessel using pelagic gears.
Fleet segments are ordered from left to right by landing volume. The overall CPUF estimates are sectored per species depending on their contribution to the total landing for each fleet-segment. The second type of fishery in this fleet segment is crustacean fisheries with landings of northern prawn Pandalus borealis with mesh sizes between mm and Norway lobster Nephrops norvegicus using mesh sizes between mm Supplementary Material Figure 5.
The brown shrimp crangon fishery using beam trawl and fine meshes mm. Crustacean fisheries show higher fuel use intensity than any other fisheries Table 1 but similar economic efficiency to the sandeel or sprat fishery. The fisheries for Northern prawn, Brown shrimp and Norway lobster showed a fuel use intensity of 1.
The total value of all crustacean species pooled was comparable to the landing for sole and cod 43 vs. However, the crustacean fishery had a much lower return in terms of volumes landed tons for all crustaceans vs. Differences in economic efficiency are therefore not very large, with 3.
This is because crustacean fisheries have a low volume of catches but high revenues, counteracting low catch rates measured in kilos per effort in the Skagerrak and Kattegat Figure 5 and Supplementary Material Figure 9. The third type of fishery in this bottom-contacting gear fleet segment is using dredges directed towards molluscs such as blue mussel Mytilus edulis.
The overall fuel consumption is not significant i. A part of the molluscs living weight accounted for in this metric is, however, not edible. Several species caught simultaneously that characterized mixed fisheries are apparent when looking at a fleet segment in particular Figure 5.
From another angle, it was also apparent that such species are also caught by several fleet segments Figure 5.
This fleet segment has significant fuel consumption and considerable effort at sea. The fleet segment has relatively high fuel intensity Table 1 , except for mm demersal seiners 0.
Therefore, the least fuel use intensive and most economically efficient fishing in this fleet segment occurred close to Danish coasts with seine and bottom trawling for cod in the Baltic Sea or gillnetting for cod, plaice and sole Figure 4.
The differences in efficiency among seining and bottom trawling for mixed demersal fisheries in the Baltic Sea compared to the North Sea and mixed crustacean fisheries reflect differences in EUR per unit effort and landing values Supplementary Material Figure 9.
Hence, the less fuel-intense trawling in the Baltic landed lower-priced fish than when seining 3. Seiners actually spent less effort landing the same landing value. On the other hand, the crustacean fishery caught higher-valued species, which also translated into higher landing values per effort Supplementary Material Figure 9.
All fleet segments using bottom-contacting gears with large meshes showed no real trend in the fuel use intensity that was stable over , except for the mm bottom trawl for demersal fish, which showed a slight increase in intensity in recent years Supp. Mat Figure 10, and coefficients of variation in Supplementary Data.
The use of pelagic gear occurred in a very large geographical area Figure 6 , and the main fleet segments exploiting pelagic species consumed an equivalent total amount of fuel compared to the main Danish fleet segments targeting demersal species. The large pelagic fleet fishing Atlantic mackerel Scomber scombrus , herring Clupea harengus , and blue whiting Micromesistius poutassou consume the most fuel across an extensive area Supplementary Figure 3.
The vessels were also operating in the most important mm fishery characterized by very low fuel use intensity 0. The fuel use intensity was the lowest 0. Figure 6 Spatial distribution of the maximal value of indicators during for landed species i.
The second-largest volume of catch comprises fishing for sprat Sprattus sprattus with a low fuel use intensity 0. Other pelagic species included horse mackerel Trachurus trachurus , sandeel with semipelagic trawl, and Norway pout Figure 8. For the herring fishery, the larger vessels were highly active in the North-East Atlantic waters beyond the FAO The spatial extent of the fisheries of the large vessels using pelagic or semipelagic gear was widespread, and the catch rate was large Supplementary Figure 6.
This makes fuel use erratically distributed spatially given that the fuel used to search for the fish pelagic schools has been allocated back to the haul positions in these maps.
All segments using pelagic gears have a stable fuel use intensity over , apart from the larger vessels with a pelagic mm gear targeting herring and active in the North Sea, which shows a large increase in average intensity when all the vessels in this category were aggregated Supplementary Material Figure The total fuel used for this fleet segment over time showed a decreasing trend in both the Baltic Sea While cod initially was the species contributing most to the overall fuel use intensity, plaice has substituted cod in most recent years.
Part of the smaller vessels specialized in targeting demersal fish for mainly cod and plaice by trawlers. Another fishery targeted molluscs such as mussels, common cockle Cerastoderma edule and oyster Ostrea spp. Some cod, plaice, dab or turbot Psetta maxima were caught by gillnetters.
Small vessels, which are smaller in size and less numerous, are characterized by much lower overall fuel consumption and catch in terms of both volume and value compared to the larger fleet segments. The spatial distribution of fuel use and subsequent catches of the smaller vessels could not be analysed due to the lack of spatial data for these segments, albeit smaller vessels were limited in their geographical range to inshore areas.
The fuel use intensity of small vessels using small meshes or gears without meshes was mainly used to catch molluscs such as cockle and mussel with a dredge Figure 7. The overall fuel use intensity estimates are sectored per species depending on their contribution to the total landing for each fleet-segment.
Small vessels using a variety of gillnets and other passive gears had a lower fuel use intensity than trawling irrespective of mesh size and outperformed bottom trawling in economic efficiency during the analysed period ca. In addition, longlining had the highest fuel use intensity among small-scale segments when targeting salmon 1.
Minor fisheries were fishing for European eel Anguilla anguilla and lumpfish Cyclopterus lumpus. Targeting for lumpfish was mainly a gillnet fishery, while eel was captured with other passive gear, such as pots, with a fuel intensity of 0. Some of these minor species in volume, such as lumpfish and sole, could have represented a large share of the economic return for these segments because they were more highly priced.
Anomalies in the time series of fishing mortalities ratios and catch-fuel efficiency ratios Supplementary Figures 19 and 20 generated some significant cross-correlations i. The dots are annual estimates and the curves depict a smoothed line obtained over each stock specific estimates.
Nevertheless, a negative correlation was found for Baltic sprat, with a lag of 1 year Supplementary Material Figure For mackerel in the North Sea, a positive correlation was instead found i. higher CPUF Figure 8. Other stocks did not show any significant correlations, albeit the signal for negative correlations i.
This study has provided essential background data and analyses of the economic efficiency and fuel use intensity of Danish fisheries, including the fuel use intensity associated with catches of different marine species by different fishing techniques and fleet segments.
Within this work, we found substantial differences in fuel use and also intensity between different fishing techniques, which confirms earlier findings that fuel consumption depends on fishing techniques and the targeted assemblage of species Parker and Tyedmers, Hence, besides saving fuel costs, one can expect more significant revenue from stocks in better shape when large, higher priced individuals could be caught for the same level of quotas.
Among larger vessels, vessel size does not seem to be an essential predictor, as such according to this study, supporting earlier findings Ziegler and Hornborg, Nevertheless, small vessels using mainly passive gear with large mesh sizes are less fuel intense than larger vessels and when conducting equivalent fisheries.
Large meshes are used to target the largest individuals making the reward high per unit of catch, added to the fact that passive gears avoid spending fuel on towing gears through the water. This affects what fishing grounds and stocks may be targeted and the portion of the stock that is vulnerable to fishing.
For example, widely distributed pelagic stocks are accessible only to very large vessels. Within fishing segments such as large vessels, there are also differences between species and gears also supported by global analysis of fuel efficiencies; Parker and Tyedmers For example, fuel use intensity was relatively low for dredge fishing on mussels, but very high in comparison for a range of crustacean fisheries; this type of fishing has relatively high fuel use intensity, as there is a low level of return i.
Nevertheless, such high fuel use is balanced against the high economic return for such fisheries. Importantly, if an absolute value for fuel consumption is utilised, those economic fleet segments targeting pelagic species show the highest fuel use, as pelagic species, being geographically spread over an extended and offshore area, require the deployment of large fishing vessels.
However, fuel use intensity for pelagic fisheries is low, i. This is because fuel use intensity is determined by litre of fuel per unit of catch kg, and the pelagic fleet is landing a huge volume compared to any other fisheries see Supplementary Data.
However, the fuel use intensity is lower for vessels targeting Norway pout, sprat and sandeel than for vessels targeting mackerel and herring, implying that target species, or perhaps gear type or fishing behaviour, are more important than vessel size.
Compared to demersal fisheries, the pelagic fishery for mackerel represents about the same economic return, but using less effort, is less fuel intense overall even if each pelagic vessel, being the largest, consumes a large amount of fuel individually.
In contrast, the demersal fisheries consume much more total fuel because the fishing is conducted by many smaller vessels, cumulating more time at sea. At the policy level, there is a need to phase out the use of fossil fuels and there are ongoing regulatory initiatives at the EU level to incentive toward this reduction EC, Phasing out fuel subsidies would have severe economic impacts on the fishing sector but would help bring forward the least fuel-dependent forms of fishing and trigger the development of alternative systems to operate the fishing.
The variability within fishing segments indicates that individual fishers may improve performance to a certain extent, and a given vessel could also belong to several segments and perform better or worse than average.
There are, however, outer boundaries to improvements defined by the availability of the stocks Bastardie et al. In the following, we discuss the four points listed here, knowing that the fuel consumption per unit of effort, which is a measure of fuel efficiency, is strictly associated with the physical vessel characteristics hull design and engine power.
In contrast, the fuel per unit of landed kg i. Hence, reducing fuel use intensity and overall fuel consumption could result from an increase in catch-fuel efficiency from different gears, mesh sizes and better stock status and from a decrease in the fuel consumed per unit of effort when operating the fishing with improved technologies.
Related to the point i , based on the results here, a change in fishing techniques towards passive gears and Danish seines may reduce fuel use intensity in Danish fisheries. However, in reducing emissions from fisheries through management actions, attention should be given to identifying effective means to assist the transition phase.
It is essential to identify possible win-win situations where synergic effects could arise albeit a change in gears will more likely come with trade-offs. For example, energy-intensive bottom trawling affects benthic habitats Rijnsdorp et al.
In the long term, the incentive for changing target species and redirecting effort from fuel-intensive fisheries e. Indeed, such an effort redirection could make existing segments unbalanced by possibly redirecting current fishing capacity towards other fisheries and impairing their future economic viability or facing nonexistent market demands.
Related to point ii , removing taxation on alternative fuels biofuels, biogas, and electricity with potential for reducing the emissions per litre of fuel consumed would also provide an incentive for their use if their price become attractive to the fishing sector EC, Most of the emission reduction potentials may be expected from implementing technical design innovations e.
However, the total water volume requirement of scenario C increased by billion gallons 4. While we have defined and investigated the potential water requirements of three future-year scenarios involving increased ethanol production, it is not clear that any of these scenarios accurately represents future developments.
However, taken together, these scenarios present a likely range of possibilities. Of these three, scenario A predicts the smallest increase in total water requirements, while scenario B predicts the largest increase. From the standpoint of water policy and management, it is important to understand the environmental and ecological impacts of these increased water requirements.
For example, withdrawing and consuming a given quantity of water is likely to have greater impacts in Kansas than in Ohio. Assessing these impacts is beyond the scope of this study.
However, our results do suggest reasons for concern, in that a significant increase in ethanol demand is likely to result in expansion of corn cropping into water-stressed areas, such as those serviced by the diminishing High Plains Aquifer.
Our modeling exercise revealed several insights into the potential water impacts of projected higher ethanol blend fuels in the USA. First, the water intensity of corn ethanol depends heavily on where the corn is grown. Ethanol produced in regions requiring significant irrigation has water intensity values two orders of magnitude higher than ethanol produced in non-irrigated areas.
Secondly, to produce significantly larger volumes of ethanol in the future will increase the water intensity in most states and will increase total water usage amounts everywhere.
Water usage is especially high in top corn-producing states that require substantial irrigation. In the three future-year scenarios explored here, it was determined that expanding corn ethanol production from Finally, to minimize water demands associated with higher corn ethanol production, expanded corn production should be concentrated in areas requiring little irrigation, and efforts should be taken to improve irrigation efficiency.
Scarlat N, Dallemand JF, Monforti-Ferrario F, Banja M, Motola V Renewable energy policy framework and bioenergy contribution in the European Union—an overview from National Renewable Energy Action Plans and Progress Reports.
Renewable Sustainable Energy Rev — Article Google Scholar. International Energy Agency Technology roadmap: biofuels for transport.
Prepared by: International Energy Agency, Paris. Escobar JC, Lora ES, Venturini OJ, Yanez EE, Castillo EF, Almazan O Biofuels: environment, technology and food security. Renewable Sustainable Energy Rev 13 — Hoekman SK Biofuels in the U. Renew Energy — doi: Committee on Economic and Environmental Impacts of Increasing Biofuels Production, National Research Council Renewable fuel standard: potential economic and environmental effects of U.
biofuel policy. Accessed Aug EPA Biofuels and the environment: first triennial report to Congress. Prepared by: National Center for Environmental Assessment, Washington. National Research Council Water implications of biofuels production in the United States.
National Academies Press, Washington D. Congress H. Renewable Fuels Association Renewable Fuels Association - Industry statistics. Accessed Feb Gruenspecht H Statement of Howard Gruenspecht, Deputy Administrator, EIA, to U.
House of Representatives Committee on Energy and Commerce. Government Accountability Office Renewable fuel standard—low expected production volumes make it unlikely that advanced biofuels can meet increasing targets. Energy Information Administration Annual Energy Outlook —with projections to Google Scholar.
Hirshfeld DS, Kolb JA, Anderson JE, Studzinski W, Frusti J Refining economics of U. gasoline: octane ratings and ethanol content. Environ Sci Technol — Leone TG, Anderson JE, Davis RS, Iqbal A, Reese RA, Shelby MH, Studzinski WM The effect of compression ratio, fuel octane rating, and ethanol content on spark-ignition engine efficiency.
Speth RL, Chow EW, Malina R, Barrett SRH, Heywood JB, Green WH Economic and environmental benefits of higher-octane gasoline. Anderson JE, DiCicco DM, Ginder JM, Kramer U, Leone TG, Raney-Pablo HE, Wallington TJ High octane number ethanol-gasoline blends: Quantifying the potential benefits in the United States.
Fuel — Service RF Another biofuels drawback: the demand for irrigation. Science — Elcock D Future US water consumption: the role of energy production. J Am Water Resour Association 46 3 — Wu M, Zhang Z, Chiu Y-W Life-cycle water quantity and water quality implications of biofuels.
Curr Sustainable Renewable Energy Rep — Chiu Y-W, Walseth B, Suh S Water embodied in bioethanol in the United States. Environ Sci Technol 43 8 — Wu M, Mintz M, Wang M, Arora S Water consumption in the production of ethanol and petroleum gasoline. Environ Manage — Dominguez-Faus R, Powers SE, Burken JG, Alvarez PJ The water footprint of biofuels: a drink or drive issue?
Environ Sci Technol 43 9 — Scown CD, Horvath A, McKone TE Water footprint of U. transportation fuels. Environ Sci Technol 45 7 — Hernandes TAD, Bufon VB, Seabra JEA Water footprint of biofuels in Brazil: assessing regional differences. Biofuels Bioproducts Biorefining-Biofpr 8 2 — Fachinelli NP, Pereira AO Impacts of sugarcane ethanol production in the Paranaiba basin water resources.
Biomass Bioenergy — Fingerman KR, Torn MS, O'Hare MH, Kammen DM Accounting for the water impacts of ethanol production. Environ Res Lett 5 :1—7. Harto C, Meyers R, Williams E Life cycle water use of low-carbon transport fuels. Energy Policy 38 9 — King CW, Webber ME Water intensity of transportation.
Environ Sci Technol 42 21 — Kreider JF, Curtiss PS Comprehensive evaluation of impacts from potential, future automotive fuel replacements.
Presented at Energy Sustainability Gerbens-Leenes W, Hoekstra AY, van der Meer TH The water footprint of bioenergy. Proc Natl Acad Sci U S A 25 — Gerbens-Leenes PW, van Lienden A, Hoekstra A, van der Meer T Biofuel scenarios in a water perspective: the global blue and green water footprint of road transport in Global Environ Change-Human Policy Dimensions 22 3 — Mekonnen MM, Hoekstra AY The green, blue and grey water footprint of crops and derived crop products.
Hydrology Earth Syst Sci 15 5 — Mekonnen MM, Hoekstra AY The green, blue and grey water footprint of crops and derived crop products, Volume 1: Main Report. UNESCO-IHE, Delft, the Netherlands. Mishra GS, Yeh S Life cycle water consumption and withdrawal requirements of ethanol from corn grain and residues.
Pfister S, Bayer P, Koehler A, Hellweg S Environmental impacts of water use in global crop production: hotspots and trade-offs with land use.
Environ Sci Technol 45 13 — footprint of bioenergy. Proc Natl Acad Sci U S A 35 :E93—E Fingerman KR, Berndes G, Orr S, Richter BD, Vugteveen P Impact assessment at the bioenergy-water nexus. Biofuels Bioproducts Biorefining-Biofpr 5 4 — Jeswani HK, Azapagic A Water footprint: methodologies and a case study for assessing the impacts of water use.
J Cleaner Prod 19 12 — Department of Agriculture. Accessed 3 Aug Renewable Fuels Association Fueling a nation: feeding the world. The role of the U. ethanol industry in food and feed production.
Prepared by: Renewable Fuels Association, Washington, DC. Mueller S Detailed Report: National Dry Mill Corn Ethanol Survey. Prepared by: Univ. Hoekstra AY, Chapagain AK, Aldaya MM, Mekonnen MM The water footprint assessment manual: setting the global standard.
Earthscan, London. Pikul JL Jr, Hammack L, Riedell WE Corn yield, nitrogen use, and corn rootworm infestation of rotations in the Northern Corn Belt. Agronomy J — Wang M, Wu M, Huo G Life-cycle energy and greenhouse gas emission impacts of different corn ethanol plant types.
Environ Res Lett 2 :1— Download references. This work was funded in part by the American Petroleum Institute API. The data collection, analysis, and interpretation were performed by the authors alone.
The results and conclusions presented here are those of the authors and do not necessarily reflect those of API. Preparation of the manuscript was done entirely by the authors. XL and SKH conceptualized the project objectives and modeling scenarios.
XL obtained the NASS data inputs and constructed the spreadsheet model to calculate the water requirements for each scenario. All three authors contributed to data analysis and writing of the final manuscript.
All authors read and approved the final manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Desert Research Institute, Raggio Pkwy, Reno, NV, , USA.
Xiaowei Vivian Liu, S. You can also search for this author in PubMed Google Scholar. Correspondence to S. Kent Hoekman. Table S1.
Table S2. Table S3. Table S4. DOCX 48 kb. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.
Reprints and permissions. Liu, X. Potential water requirements of increased ethanol fuel in the USA. Energ Sustain Soc 7 , 18 Download citation.
Received : 16 December Accepted : 22 May Published : 21 June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.
Skip to main content. Search all BMC articles Search. Download PDF. Original article Open access Published: 21 June Potential water requirements of increased ethanol fuel in the USA Xiaowei Vivian Liu 1 , S. Abstract Background To mitigate climate impacts associated with energy consumption, renewable fuel policies have been established in the USA that encourage production and use of corn ethanol.
Methods A simple modeling approach was used to assess the water requirements for producing Conclusions Increasing ethanol blend fuels from E10 to E20 in the near future will require significant expansion of corn cropping in the USA, which will increase irrigation demands.
Background For reasons of greenhouse gas GHG mitigation, energy security, and domestic energy supply, greater use of biofuels is being promoted in many countries throughout the world [ 1 , 2 , 3 , 4 ]. Current water requirements for corn ethanol Although water usage of corn-ethanol production plants is rather modest overall, this can still represent a significant additional demand in a local region.
Full size image. Life-cycle consumptive water use of corn-ethanol and petroleum-derived gasoline. Methods To produce substantially more corn ethanol in the USA will require increased water usage.
Scenario definition The NASS database includes information about corn production on irrigated and non-irrigated land in 29 corn-growing states. Table 1 Description of scenarios investigated in this study Full size table.
Table 2 Model input parameters used in baseline and future-year scenarios Full size table. Results and discussion Before discussing the modeling results, three caveats related to corn yields should be mentioned. Water intensity and total water use—baseline scenario In the baseline scenario, it is assumed that the water use efficiency is the same in all ethanol plants, namely, 2.
Table 3 Statewide crop production, ethanol production, and water use data for baseline scenario Full size table. Conclusions Our modeling exercise revealed several insights into the potential water impacts of projected higher ethanol blend fuels in the USA.
References Scarlat N, Dallemand JF, Monforti-Ferrario F, Banja M, Motola V Renewable energy policy framework and bioenergy contribution in the European Union—an overview from National Renewable Energy Action Plans and Progress Reports.
Renewable Sustainable Energy Rev — Article Google Scholar International Energy Agency Technology roadmap: biofuels for transport. Prepared by: International Energy Agency, Paris Escobar JC, Lora ES, Venturini OJ, Yanez EE, Castillo EF, Almazan O Biofuels: environment, technology and food security.
Renewable Sustainable Energy Rev 13 — Article Google Scholar Hoekman SK Biofuels in the U. Accessed Aug EPA Biofuels and the environment: first triennial report to Congress. Accessed Aug Renewable Fuels Association Renewable Fuels Association - Industry statistics.
Accessed Feb Gruenspecht H Statement of Howard Gruenspecht, Deputy Administrator, EIA, to U. Accessed Feb U. GAO U. pdf Google Scholar Hirshfeld DS, Kolb JA, Anderson JE, Studzinski W, Frusti J Refining economics of U.
Environ Sci Technol — Article Google Scholar Leone TG, Anderson JE, Davis RS, Iqbal A, Reese RA, Shelby MH, Studzinski WM The effect of compression ratio, fuel octane rating, and ethanol content on spark-ignition engine efficiency.
Science — Article Google Scholar Elcock D Future US water consumption: the role of energy production. J Am Water Resour Association 46 3 — Article Google Scholar Wu M, Zhang Z, Chiu Y-W Life-cycle water quantity and water quality implications of biofuels.
Curr Sustainable Renewable Energy Rep —10 Article Google Scholar Chiu Y-W, Walseth B, Suh S Water embodied in bioethanol in the United States. Environ Sci Technol 43 8 — Article Google Scholar Wu M, Mintz M, Wang M, Arora S Water consumption in the production of ethanol and petroleum gasoline.
Environ Manage — Article Google Scholar Dominguez-Faus R, Powers SE, Burken JG, Alvarez PJ The water footprint of biofuels: a drink or drive issue? Environ Sci Technol 43 9 — Article Google Scholar Scown CD, Horvath A, McKone TE Water footprint of U.
Environ Sci Technol 45 7 — Article Google Scholar Hernandes TAD, Bufon VB, Seabra JEA Water footprint of biofuels in Brazil: assessing regional differences. Biofuels Bioproducts Biorefining-Biofpr 8 2 — Article Google Scholar Fachinelli NP, Pereira AO Impacts of sugarcane ethanol production in the Paranaiba basin water resources.
Biomass Bioenergy —16 Article Google Scholar Fingerman KR, Torn MS, O'Hare MH, Kammen DM Accounting for the water impacts of ethanol production.
Environ Res Lett 5 :1—7 Google Scholar Harto C, Meyers R, Williams E Life cycle water use of low-carbon transport fuels. Energy Policy 38 9 — Article Google Scholar King CW, Webber ME Water intensity of transportation. Environ Sci Technol 42 21 — Article Google Scholar Kreider JF, Curtiss PS Comprehensive evaluation of impacts from potential, future automotive fuel replacements.
Presented at Energy Sustainability Gerbens-Leenes W, Hoekstra AY, van der Meer TH The water footprint of bioenergy. Proc Natl Acad Sci U S A 25 — Article Google Scholar Gerbens-Leenes PW, van Lienden A, Hoekstra A, van der Meer T Biofuel scenarios in a water perspective: the global blue and green water footprint of road transport in Global Environ Change-Human Policy Dimensions 22 3 — Article Google Scholar Mekonnen MM, Hoekstra AY The green, blue and grey water footprint of crops and derived crop products.
UNESCO-IHE, Delft, the Netherlands Google Scholar Mishra GS, Yeh S Life cycle water consumption and withdrawal requirements of ethanol from corn grain and residues. Environ Sci Technol — Article Google Scholar Pfister S, Bayer P, Koehler A, Hellweg S Environmental impacts of water use in global crop production: hotspots and trade-offs with land use.
Biofuels Bioproducts Biorefining-Biofpr 5 4 — Article Google Scholar Jeswani HK, Azapagic A Water footprint: methodologies and a case study for assessing the impacts of water use. J Cleaner Prod 19 12 — Article Google Scholar National Agricultural Statistics Service, U.
Fuel efficiency or fuel fue is a form Epevated thermal efficiencymeaning the ratio of effort to result of a potdntial that converts chemical potential energy contained in a carrier fuel potentizl kinetic energy potentisl work. Obesity prevention awareness fuel Elevzted may Elevaed per Hair growth shampoo, which in turn may vary per application, utipization this Body composition testing of Hair growth shampoo Eevated often illustrated as Elevated fuel utilization potential continuous energy profile. Non-transportation applications, such as industrybenefit from increased fuel efficiency, especially fossil fuel power plants or industries dealing with combustionsuch as ammonia production during the Haber process. In the context of transportfuel economy is the energy efficiency of a particular vehicle, given as a ratio of distance traveled per unit of fuel consumed. It is dependent on several factors including engine efficiencytransmission design, and tire design. Fuel consumption is a more accurate measure of a vehicle's performance because it is a linear relationship while fuel economy leads to distortions in efficiency improvements. Fuel efficiency is dependent on many parameters of a vehicle, including its engine parameters, aerodynamic dragweight, AC usage, fuel and rolling resistance. Our ability to Elecated, bicycle, Elevated fuel utilization potential, Muscle recovery foods, and row hinges on the uitlization of the body to extract Plant-based nutrition for endurance athletes from Hair growth shampoo food. As potential fuel sources, potetnial carbohydrate, fat, Elevatd protein in the foods that you potenntial follow different fueo Elevated fuel utilization potential in the body, ufel they all ultimately yield water, carbon dioxide, and a chemical energy called adenosine triphosphate ATP. Think of ATP molecules as high-energy compounds or batteries that store energy. Anytime you need energy—to breathe, to tie your shoes, or to cycle miles km —your body uses ATP molecules. ATP, in fact, is the only molecule able to provide energy to muscle fibers to power muscle contractions. Creatine phosphate CPlike ATP, is also stored in small amounts within cells. It's another high-energy compound that can be rapidly mobilized to help fuel short, explosive efforts.
0 thoughts on “Elevated fuel utilization potential”