Journal of Cleaner Production 135 (2016) 523e532 Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro The moderating role of biomass availability in biopower co-firing d A sensitivity analysis Zuoming Liu a, *, Thomas G Johnson b, Ira Altman c a Lynchburg College, Department of Management, School of Business and Economics, 1501 Lakeside Drive, Lynchburg, VA 24501-3113, USA University of Missouri-Columbia, Department of Agricultural Economics, 215 Middlebush Hall, Columbia, MO 65211, USA c Southern Illinois University-Carbondale, Department of Agribusiness Economics, Mail Code 4411, 1205 Lincoln Drive, Carbondale, IL 62901, USA b a r t i c l e i n f o a b s t r a c t Article history: Received 16 October 2015 Received in revised form 21 May 2016 Accepted 17 June 2016 Available online 19 June 2016 Of the various types of renewable energy technologies being promoted in response to concerns about climate change and energy security, co-firing biomass for electricity is one that is potentially feasible in many states and regions of the USA This study contributes to our understanding of the factors that influence the economic feasibility of this technology Using a recently developed spatial evaluation tool we perform sensitivity analyses to investigate how the cost of co-firing biomass is affected by power plant scale, level of biomass used as feedstock, local feedstock availability, transportation costs, and resource and harvesting costs Specifically, we demonstrate the use of this tool by exploring the cost of co-firing biomass in existing qualified coal-fired power plants in Missouri We find that the cost of electricity generated is higher when biomass is cofired under all assumption However, it finds significant and interesting interaction among the cost-related features We are able to conclude that abundant and reasonably-priced biomass feedstocks can dramatically increase the feasibility of biopower by reducing transportation costs Also, the scale of the technology must be rightdlarge enough to exploit economies of scale but small enough to avoid high transportation costs incurred to procure large volumes of feedstocks © 2016 Elsevier Ltd All rights reserved Keywords: Biomass Co-firing Biopower Linear programming Sensitivity analysis Introduction Two days before the United Nations summit on climate change on September 21st 2014, one of the largest ever climate-change demonstrations, estimated to involve more than 300,000 people, took place in the streets of New York City (USA Today, 2014) Large protests were held in other locations as well These demonstrations sent a strong message that more and more people are concerned about climate change On the other hand, given the world's overwhelming dependence on low-cost fossil fuels, there are also concerns about the possible damage to the economy that switching from fossil fuels to renewable energy could cause In early September 2014, a report entitled “Better Growth, Better Climate: The New Climate Economy Report”, was released by the Global Commission on the Economy and Climate The Commission included more than 100 politicians, leaders, economists and other * Corresponding author E-mail addresses: lzuoming@gmail.com (Z Liu), JohnsonTG@missouri.edu (T.G Johnson), ialtman@siu.edu (I Altman) http://dx.doi.org/10.1016/j.jclepro.2016.06.101 0959-6526/© 2016 Elsevier Ltd All rights reserved scientists from seven countries The report argued that it is possible to reduce the risk of climate change while achieving economic growth (GCEC, 2014) Despite recent dramatic increases in the production of domestic oil and natural gas, concerns about energy sustainability and security continue to be raised (WEC, 2007; EIA, 2013) In June 2014, the U.S Environmental Protection Agency (EPA) proposed guidelines designed to reduce the national level of CO2 emissions from power plants by 30% from 2005 levels by 2030 Strategies to reach this goal will be developed and executed at the state level, and each state is required to submit CO2-reduction plan by 2016 (EPA, 2014) A study by the University of Massachusetts Political Economy Research Institute (PERI) and Center for American Progress in September 2014 declared that 40% of 2005 levels of carbon pollution could be eliminated, and 2.7 million jobs related to clean energy could be created at the same time (Pollin et al., 2014) In response to these findings, more and more research is being undertaken to find clean, safe and renewable energy sources to complement or even replace fossil fuels Biomass-based energy (bioenergy) has significant appeal as a partial replacement for fossil fuels because it is renewable, emits less carbon into the 524 Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 atmosphere, is potentially more environmentally benign, is easier to procure and store, and is almost ubiquitous Biopower is one popular use of biomass with better energy utilization than biofuels (Mizsey and Racz, 2010) Biopower technology offers local benefits as a way of disposing residues and wastes, and global benefits by reducing greenhouse emissions (Yusoff, 2006) A great deal of research has focused on the technical aspects of biopower production such as optimum oxygen factors, air temperature, air-fuel ratio, operating pressure, biomass particle size, pressure, etc Bioenergy research and practitioners have confirmed that co-firing biomass in existing plants, especially coal-powered plants, is a technically feasible option (Ponton, 2009) While biomass residues can replace more than 50% of coal in coal-fired plants with large capital investments (English et al., 1981), up to 20% biomass can be co-fired with coal without significant modification to current equipment (Grabowski, 2004; Haq, 2002) Biomass use must to be managed very carefully to avoid decreased boiler efficiency (English et al., 2007; English, 2010) and boiler corrosion In this article, we focus on 10% and 15% biomass co-firing levels and analyze the impacts of non-technical factors such as fuel availability and transportation costs on the feasibility of biopower generation in the Midwestern U.S state of Missouri Specifically, we conduct sensitivity analyses of varying levels of biomass availability, transportation costs and biomass resource and harvesting costs on the economic feasibility of co-firing in existing coal-powered plants in Missouri In Missouri, about 90% of the total electricity supply comes from investor-owned plants Based on data from the U.S Energy Information Administration (EIA, 2014), in 2013, 83% of Missouri's electricity generation came from coal compared to the national average of about 45% Another 9% of electricity was supplied by nuclear power, mainly from the Callaway Nuclear Generating Station, and about 3% of electricity generation came from renewable energy resources, with about 95% of that from conventional hydroelectric power and wind Only a small portion of electricity was generated from biomass, mainly at two low-capacity biopower plants, the University of Missouri (18 Megawatts or MW) and Anheuser Busch St Louis (26 MW) (EIA, 2014) However, given Missouri's abundant biomass resources from agriculture and forestry sectors, there is significant potential for more biopower production As a major agricultural state, with large quantities of crop residues and promising prospects for energy crops, as well as large areas of productive forests, Missouri produces vast amounts of biomass each year, some of which could be used for biopower generation The Missouri Department of Natural Resources has estimated that 172,550,603 megawatt hours (MWh) could be produced annually This is almost twice the total electricity produced in Missouri in 2009 (Fink and Ross, 2006) Although biomass feedstocks can only be partially collected and used, they nevertheless offer great potential for increased renewable energy generation and reductions in carbon emissions within the state In 2008, Missouri adopted a renewable portfolio standard (RPS), requiring investor owned electric utilities to increase their use of renewable energy sources to 15% by 2021 With proposed guidelines from the U.S EPA in 2014 to reduce the national level of CO2 emissions from power plants 30% by 2030, it is imperative for the power plants in the state to diversify their fuel mix by including more renewable energy resources Co-firing biomass in existing coal-powered plants can help the owners meet the RPS requirements and can be an incremental way of reducing the emission of greenhouse gas and other pollutants It is in this context that this study investigates the role of several factors in shaping the economic feasibility of biomass co-firing in Missouri, with the aim of identifying the most critical factors determining the ideal locations, scales, and feedstocks for power generation in Missouri The tool and method employed in this analysis can be adapted to any state or region contemplating an increase in biopower capacity Literature review Compared with traditional fossil fuels, the supply fluctuations and low energy density features of biomass feedstocks are major deterrents for large-scale biopower generation (Akhtari et al., 2014) Biopower plants usually have small capacities, typically one-tenth the size of coal-fired plants, due to the limited availability of local feedstocks (IEA , 2007) Due to region-specific variations in feedstock, transportation costs and many other economic parameters in biopower generation are not known with certainty, and the cost of this process varies across regions (Schneider and McCarl, 2003) So conducting a sensitivity analysis over a wide range of cost assumptions has important practical implications Detailed information regarding the forces that impact the feasibility of biopower production is useful for industry strategists, policy makers, and bioenergy entrepreneurs As a result, many national and regional level studies have been conducted to assess the economic feasibility and/or environmental consequences involved in using bioenergy Given the inevitable uncertainty involved in locating a new facility, sensitivity analysis is a useful tool for identifying the most critical factors to consider Sensitivity analysis has been widely employed in environmental and biomass related fields Mathieu and Dubuisson (2002) simulated the process of wood gasification in the ASPEN PLUS process simulator based on the Gibbs free energy minimization, and conducted a sensitivity analysis on various factors regarding their effects on process efficiency, such as oxygen factors, air temperature, oxygen content in air, operating pressure and the injection of steam Bettagli et al (1995) calculated the gas composition under alternative operating conditions using a model to simulate the chemical kinetics of gasification and combustion processes In their study, they performed a sensitivity analysis to evaluate the influence of the major parameters involved, such as temperature, pressure, and air-fuel ratio on the composition of the exit gas Schuster et al (2001) used thermodynamic equilibrium calculations to simulate a dual fluidized-bed steam gasifier with a decentralized system that combined heat and power They conducted a sensitivity analysis of the process for a wide range of fuel composition levels and various operating parameters, and found that the most significant factors that determine the chemical efficiency of the gasification are gasification temperature and fuel oxygen content Another study regarding biomass gasification in a fluidized bed by Lv et al (2004) involved a sensitivity analysis to investigate how the gas quality is influenced by many technical factors including temperature, steam to biomass ratio, biomass particle size, gas yield, steam decomposition, heating value, etc Their results indicate that a tradeoff exists between hydrogen production and gas heating value as temperature changes, and that optimal steam level and small size of particles can improve gas quality Sadaka et al (2002) built a two-phase biomass gasification model and conducted sensitivity analysis to test the model's response to alternative operating parameters (fluidization velocity, steam flow rate and biomass to steam ratio) The analysis showed that all operating parameters impact the model performance, and that the steam flow rate has a larger impact on the reactor's temperatures than the other two parameters Although there are many biomass-related sensitivity analyses, most focused on the impacts of various technical factors, such as air temperature, oxygen content, operating pressure, etc There are very few studies that investigate how the performance of biopower is related to non-technical, economic factors, such as input costs and electricity prices involved in biopower generation Dornburg Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 and Faaij (2001) analyzed the energetic and economic performance of various bioenergy systems with respect to energy savings compared with fossil energy The performance was studied for a number of capacity scales using parameters such as cost of investment and prices of heat and electricity They varied these parameters to determine how and to what extent the performance results are influenced Another sensitivity study was performed by Monge et al (2014) to study the impact of capital and operating expenses on the feasibility of using three types of biofuel technologydHydrolysis, Pyrolysis, and Gasification They also performed a sensitivity analysis of biofuel conversion yields on feasibility, and discount rate on net present value of returns Bazmi et al (2015) built a decentralized energy generation optimization model to study an energy generation system using palm oil which considered various costs such as biomass acquisition, operation, capital, transportation, as well as electricity transmission costs Sensitivity analyses have also been conducted to determine the role of cost and location related factors Most of these have been focused on biofuels (see for example, Jain et al., 2010; Wright et al., 2010) Other sensitivity analyses have studied the factors affecting the feasibility of direct incineration of biomass for electrical production with most of these focusing on forest residues, and dedicated biomass crops in smaller scale plants (see for example Cucchiella et al., 2015; Hacatoglu et al., 2011; Thakur et al., 2014) However, under the pressure and guidelines of RPS and EPA regarding the increase of renewable energy use and reduction of CO2 emission, converting coal-fired power plants to co-fire plants for most states would be a good starting attempt without large capital commitment Motivation of the study The basic motivation of this study is to identify the impact of key economic factors in biopower generation, and provide useful information to guide investors as they make decisions regarding the location and size of biopower investments Many factors involved in biopower production affect the competitiveness of biopower evis conventional coal-fired generation Technological advisea vances in biopower production could significantly change the cost structure of producing biopower in the long run, but in the short term factors such as biomass availability, transportation costs, capital costs, economies of scale, etc determine the competitiveness of biopower In this article, we conduct several sensitivity analyses to test how changes in key economic factors impact the production costs of co-firing biomass in existing coal-powered plants Specifically, the influence of biomass feedstock availability, transportation costs, and biomass resource and harvesting costs will be investigated Although bioenergy is one of the largest sources of renewable energy, most biomass resources are widely distributed At present, biomass is relatively costly to collect, store and transport, especially in view of its low energy density (Akhtari et al., 2014) Traditional fossil fuel suppliers have developed cost-effective supply chains and logistics processes while most biomass markets have yet to fully develop Moreover, the highly seasonal nature of many types of biomass requires extra effort and expenditures to maintain the continuity of feedstock supply Solutions to the supply continuity issue include such strategies as diversifying the portfolio of biomass feedstocks or increased storage capacity to even out the supply fluctuations Transportation costs are another critical factor in biopower generation The bulky nature and low-energy density of biomass feedstocks make transportation costs one of the major obstacles in biopower generation (Gold and Seuring, 2011) High transportation costs resulting from long hauling distances often prevent biomass from becoming a feasible feedstock Generally 525 speaking, it is not economically viable to haul biomass fuels over 100 miles (Bechen, 2011) As Shakya (2007) noted, the locations of most existing biomass power plants are in places where abundant cheap biomass feedstocks exist or where biomass residues may otherwise incur disposal costs, such as in sugar milling, wood factories and paper mills The third key factor included in our sensitivity analysis is the cost of the biomass itself The cost of the biomass feedstocks to the power plant includes the in situ opportunity cost of the feedstocks plus the cost of harvesting This study analyzes biomass co-firing in Missouri The analysis in this study contributes to the literature by exploring the interacting impacts of biomass feedstock availability and key cost factors on the total cost of producing electricity in conventional coal-fired power plants It is complementary to most previous studies which focus on the feasibility of technology-related factors and dedicated biomass power plants This study also takes an explicitly placebased approach in which the local conditions ultimately determine the feasibility of co-firing biomass in any particular location The method developed for this article will allow policy makers, bioenergy industry developers and entrepreneurs to determine the feasibility of co-firing biomass given any location's agronomic, climatic, geographic and transportation infrastructure characteristics Methodology and data This study employs a linear programming (LP) model developed by Liu et al (2014) to conduct the sensitivity analyses regarding the impacts of variations in the availability of biomass feedstocks, transportation costs and resource and harvesting costs (R&H) Six general scenarios were developed in Liu et al (2014), two biomass co-firing levels (10% and 15%),1 and three assumptions regarding feedstocks availability (10%, 20% and 30%) The objective function of their model is to minimize total costs involved in the co-firing process, including both fixed and variable costs Typical costs include costs of operation and maintenance, transportation, handling and processing, and storage Annual depreciation and revenue of electricity sale are also incorporated in the objective function The decision variable in this LP model is the quantity of biomass feedstock procured from each location in the region Five major constraints are specified in the model The first constraint is that the total supply must be no less than the total demand The second constraint is that the installed capacity must exceed the actual demand by a certain percentage This extra or excess capacity is called Peak Reserve Factor which is designed to safeguard against possible electricity shortfalls due to unpredicted events The third constraint limits the power plants' emissions, of environmental pollutants to levels below some upper bound The fourth is feedstock constraint ensuring that the amount of feedstock used does not exceed the available amount The last constraint is the energy requirement constraint, which ensures that the energy used to produce a certain amount of electricity does not exceed the total energy contained in the biomass feedstocks used.2 The LP model of Liu et al (2014) is summarized in Appendix A Intensive data were used in the linear programming model of Liu et al (2014) The first type of data includes the characteristics of power plants and co-firing technologies, obtained from the State Energy Data System (SEDS) in the U.S Energy Information Administration (EIA) The second type of data is related to the various types of biomass feedstock available and their Give the technologies assumed by Liu et al (2014), there is little or no efficiency loss when up to 15% of energy is provided by biomass (NREL, 2000; Tillman, 2000) The energy constraint reflects the lower energy density when biomass is cofired with coal 526 Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 Table Summary of scenarios with specifications Scenario Co-firing level (% of energy supplied) Biomass availability (% of annual regional production) Transportion costs (TC) R&H costs Baseline Liu et al (2014) TC ± 10% R&H ± 10% TC ± 10% R&H ± 10% 10 11 12 13 14 15 16 17 18 19 10 21 22 23 24 25 26 27 28 29 30 10 10 10 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 15 15 15 15 15 15 15 15 15 15 15 15 10 20 30 10 20 30 10 10 20 20 30 30 10 10 20 20 30 30 10 10 20 20 30 30 10 10 20 20 30 30 characteristics, availability, prices and transportation costs etc These data were collected from the Missouri Department of Natural Resources as well as the Biomass Site Assessment Tools in BioSAT (www.biosat.net) The third type of data is the electricity demand and environmental emission restrictions, which were obtained from the U.S Environmental Protection Agency (EPA), EIA and Missouri Public Service Commission The fourth type of data is related to various costs involved in co-firing, which were collected from several sources including Oak Ridge National Laboratory (ORNL) and Energy Technology Systems Analysis Program (ETSAP) of International Energy Agency (IEA) In this study, we mainly focus on the second type of data to carry out the sensitivity analyses As indicated earlier, we are interested in the impacts of several key economic factors on the total costs of co-firing Specifically, the influence of biomass feedstock availability, transportation costs, and biomass resource and harvesting costs will be analyzed in the sensitivity analyses, using the model developed by Liu et al (2014) We use the six scenarios developed in Liu et al (2014) as baseline, and varied transportation costs (TC) and resource & handling (R&H) costs 10% above and below those of the baseline scenarios Overall, 30 scenarios were conducted to complete the sensitivity analyses: (baseline) scenarios, and 24 in which we varied the transportation costs and R&H costs These scenarios are summarized in Table Results and discussion The LP models in this study were solved using AMPL3 The value of the cost-minimizing objective function and optimal levels of all A Mathematical Programming Language for describing data optimization with variables, objectives, and constraints (ampl.com) TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC TC À þ À þ À þ À þ À þ À þ 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H R&H À þ À þ À þ 10% 10% 10% 10% 10% 10% À þ À þ À þ 10% 10% 10% 10% 10% 10% decision variables were calculated by the model for each scenario The detailed results are reported in the Appendix B 5.1 Analysis of sensitivity to biomass availability Consistent with previous studies, such as English et al (2007) and Liu et al (2014), the simulations show that it costs more to use biomass fuel for electrical generation than coal, even though the average cost of the biomass feedstock is lower than coal Transportation costs play a major role in contributing to higher total costs Not surprisingly, the results also indicate that the total cost of co-firing biomass decreases continuously as the available supply of biomass feedstocks increases from 10% to 30% of total local resources This relationship is true for both 10% and 15% levels of biomass in total fuel consumption This pattern is shown in Fig The lower cost of production achieved when larger proportions of local biomass resources are supplied results from savings in transportation costs due to the shorter hauling distances needed to procure sufficient feedstocks Meanwhile, although co-firing biomass does increase production costs at both 10% and 15% biomass levels, the extra costs associated with biomass use decrease as the availability of biomass increases (Fig 2) As explained above, this negative relationship between cost and availability of biomass is caused by the lower transportation costs realized when more feedstocks are accessed at shorter distances Furthermore, the difference in extra costs between 10% and 15% co-firing levels declines (Fig 2) with increases in biomass availability In other words, the cost moderating effect of higher rates of biomass availability is greater at higher biomass mix rates This finding is perhaps not surprising, but it is also not tautological It is quite possible that rising co-firing levels could mitigate the advantages of higher rates of resource availability This interesting Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 527 Fig Total costs at 10% & 15% biomass co-firing levels Fig Extra Costs & Different for 10% & 15% biomass co-firing levels relationship between co-firing levels and rates of biomass availability results from the interactions among three types of costs First, transportation costs rise with co-firing rates because more biomass feedstocks must be transported longer distances The second factor is the lower cost of biomass feedstocks compared with coal In these scenarios the price of coal is assumed to be $5.43/MWh based on data from EIA (2009), compared to $4/MWh for biomass feedstock calculated based on the LP model Therefore, co-firing 15% biomass, results in more fuel cost savings than 10% because more low cost biomass fuels are used When biomass availability is low, i.e.10% of local resources, the savings in fuel costs will be offset by the high transportation costs because of longer hauling distance But when more abundant biomass feedstocks are available nearby, i.e 20% or 30% availability, the saving in feedstock costs will play a larger role in overall production costs Higher rates of biomass use increase the arithmetic weight on biomass price in the calculation of total production costs The third cost factor leading to this finding is capital costs Using larger amounts of lowpriced biomass feedstocks attenuates the capital investment costs involved in co-firing biomass and further reduces overall production costs These economies of scales are most effective when both the rate of biomass use is high and the local availability is high Thus it will be very important to the ultimate feasibility of biopower that active and dependable markets for biomass be established Depending on the relative bargaining power of the buyers and sellers, the economic rent associated with proximity to the plant will be split in some way between the power plants and the biomass producers The power plants will benefit most from higher availability rates close to the plant and would therefore have an incentive to bid up the price of biomass close to their plants 5.2 Analysis of sensitivity to transportation and R&H costs As discussed earlier, transportation costs are a critical factor in biopower generation due to the bulkiness and low energy density of biomass feedstocks Another major cost component is the cost of buying the biomass resources, which includes the price of the feedstocks and the cost of harvesting We conducted sensitivity analyses on these two types of variable costs to determine how the total costs and extra costs are affected if transportation costs and R&H costs vary 10% above or below the baseline assumptions The results of these sensitivity analyses are summarized in Table As in the baseline, the total generation costs for all scenarios are higher when co-firing biomass than coal firing only Also as before, costs fall as the availability of biomass increases and rises as the rate of co-firing increases These relationships are shown in Figs and As expected, the total generation costs increase as the transportation costs and R&H costs increase The goal of this analysis was not to determine the direction of the impacts but rather the relative magnitudes We find that for both the 10% and 15% co-firing levels, the cost differences among the three transportation cost assumptions (baseline, 10% below and 10% above) decline as the biomass availability increases In Fig the steeper lines associated with higher transportation costs indicates that transportation costs are disproportionately moderated by higher biomass availability Increasing the level of biomass availability thus reduces the significance of variability in transportation costs We are able to conclude that total cost of adopting biomass is more sensitive to transportation costs when biomass availability is low Thus the feasibility of co-firing will be relatively more responsive to 528 Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 Table Total co-firing cost and extra cost under alternative scenarios Cost change Total Cost ($) Extra Cost ($) Total Cost ($) Extra Cost ($) TC-10% TC (Baseline) TCþ10% TC-10% TC (Baseline) TCþ10% R&H-10% R&H (Baseline) R&Hþ10% R&H-10% R&H (Baseline) R&Hþ10% 10% Biomass co-firing level 15% Biomass co-firing level 10% available 20% available 30% available 10% available 20% available 30% available 5,354,406 5,586,569 5,842,085 1,489,022 1,731,185 1,986,701 5,279,736 5,586,569 5,893,403 1,424,352 1,731,185 2,038,018 5,148,775 5,258,005 5,347,585 1,293,391 1,402,620 1,492,201 4,966,483 5,258,005 5,539,075 1,111,098 1,402,620 1,683,690 5,064,742 5,155,804 5,231,939 1,209,358 1,300,419 1,376,554 4,957,809 5,155,804 5,426,681 1,102,425 1,300,419 1,571,297 8,192,598 8,395,226 8,597,669 2,409,521 2,612,149 2,814,593 7,944,276 8,395,226 8,846,175 2,161,200 2,612,149 3,063,099 7,767,340 7,934,935 8,085,081 1,984,264 2,151,859 2,302,004 7,495,172 7,934,935 8,355,036 1,712,096 2,151,859 2,571,960 7,562,424 7,711,423 7,860,452 1,779,348 1,928,347 2,077,376 7,275,602 7,711,423 8,145,978 1,492,526 1,928,347 2,362,902 Fig Total costs under different transportation costs for 10% and 15% co-firing levels increasing the local availability of biomass than to reducing unit transportation costs However, the converging (moderating) pattern is much less obvious for the changes in R&H costs (see Fig 4) This is because the R&H costs not interact as much with biomass availability as transportation costs As to the extra costs of co-firing biomass under different transportation costs (or R&H costs), they generate similar patterns to those in the base scenarios In Figs and the lines associated with 15% co-firing level are all steeper than those associated with 10% co-firing That is, as the availability of biomass increases, the difference in extra costs between the 10% and 15% co-firing levels declines because of the decrease in transportation costs at shorter hauling distances and the savings in fuel costs by using lower- priced biomass feedstocks As in the baseline scenarios, high biomass availability reduces the negative impact of high transportation costs, and benefits from the lower-priced biomass feedstocks especially when biomass is used at higher mix rates For low biomass co-firing levels, benefits of using lower-priced biomass are positive but less significant Conclusion Growing concerns regarding the adverse effects of using fossil fuels have drawn attention to biomass-based energy because it is renewable, ubiquitous, generally environmentally friendly, easily handled and stored, and because it leads to reductions in carbon emissions The use of biomass feedstocks as a substitute for coal in Fig Total costs under different R&H costs for 10% and 15% co-firing levels Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 529 Fig Extra costs for 10% and 15% biomass co-firing under TC ± 10% Fig Extra costs for 10% and 15% biomass co-firing under R&H ± 10% electricity generation can significantly reduce emissions of greenhouse gases such as carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOx), and methane In Missouri, where more than 80% of electricity comes from coal-fired power plants, it is inevitable that the state will diversify its electricity generation capacity, given the EPA's guidelines on carbon reduction In contrast to most previous research which has investigated technological factors in biopower generation, this research studied the impacts of biomass availability and variations in transportation, and resource and hauling costs on the total power generation costs when co-firing biopower The research reported here does not contradict previous research but rather complements it This research shows that given knowledge of regionspecific transportation, agricultural, forestry, climatic, and geographic characteristics, investors and policy makers can make better decisions about the optimal location and scale of future biopower capacity It also indicates the sensitivity of these decisions to variability in the key factors determining the feasibility of co-firing biomass Overall, given the current price of coal, current technology, transportation costs, availability of biomass, and policy, co-firing biomass for electricity in Missouri is not yet economically feasible with subsidies Total generation costs are higher when co-firing biomass for all locations and all scenarios analyzed Until technological innovations or changes in the basic costs of coal, biomass, and transportation change, policy intervention will be necessary to significantly increase biomass-fueled electricity generation Possible policy options include capital or operating subsidies, tax credits, cap and trade programs, carbon taxes, and promotion of green tag programs Government funding for R&D related to advanced biopower may also help to improve biopower technology and reduce the associated costs in the long run but economic incentives will be necessary to achieve increased adoption of biopower adoption in the short term This research also suggests where technical research may yield the greatest returns Because of the low-energy density and bulkiness of biomass feedstocks, the feasibility of biopower is highly sensitive to transportation costs Based on the sensitivity analyses in this study, costs associated with biomass co-firing decrease as the nearby availability of biomass feedstocks increase, due to lower transportation costs Biomass availability moderates the impacts of transportation costs on feasibility of biopower With low levels of biomass availability, the total cost of co-firing biomass is more sensitive to transportation costs Feasibility is less sensitive to fixed capital costs, resource costs, and operating costs Thus, research that leads to reduced transportation costs will potentially have larger impacts on economic feasibility of biopower than other types of technical research In addition, more concentrated availability of biomass is likely to have a significant impact on economic feasibility When biomass availability is low, transportation costs offset the price advantage that biomass feedstocks have over coal More concentrated availability of biomass in areas close to the power plant disproportionately reduces transportation costs This effect is more significant when biomass is utilized at higher levels for two reasons On the one hand, more cost savings are possible when using larger amounts of the lower-priced biomass On the other hand, greater reliance on biomass attenuates the biopower-related capital investment Therefore, biomass must be used at just the right leveldhigh enough to take advantage of the low-priced feedstock and to exploit economy of scale, but low enough that transportation costs not increase total costs too much This ‘sweet 530 Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 spot’ will differ from place to place depending on all of the factors cited above Appendix A LP model adapted from Liu et al (2014) Objective function: Min Limitation and future research This study considers the costs of biomass co-firing technology in the coal-powered plants in Missouri where biopower is most likely to become feasible However, co-firing biomass in current coal-fired power plants is probably only a short-run response to our goals of reducing carbon emissions and increasing sustainability In the long run, biomass-dedicated power plants will be more effective means of achieving these goals Furthermore, biopower production itself is only one tool in the climate change mitigation toolkit A complete solution will require many other changes in energy demand and supply Converting today's coal-fired power plants into co-firing facilities is only a transitional strategy As we invest in additional generation capacity, and as older plants become obsolete, new, biomass only plants will be needed Other research is considering the technical and economic feasibility of new biopower plants but was not the focus of this study An important extension of the current research would be to conduct more dynamic analysis of biopower The model employed in this study is static with annual-based analysis which assumes that the parameters included in the model, such as prices, costs, etc., are stable across the whole year In practice, the values of those parameters are very unlikely to stay constant A dynamic model could simulate the entire electricity generation system in a state or region given a time horizon allowing changes in demand and price of electricity due to the growth in population Again, the purpose of the current study was demonstrate the importance of local characteristics and the interaction between local biomass availability, generator scale, and planned level of biomass use when considering conversion to co-firing biomass The impacts and interactions studies here may still exist in the dynamic model Ignored in this sensitivity analysis are the impacts of co-firing biomass on the local economy and community As Altman et al (2007) have shown, bioenergy development can generate significant benefits in the local economy by creating new jobs and markets, and adding extra incomes to the local community, especially in the long run when a mature biopower industry forms in the economy If the demand for biomass is sufficiently large and stable, a market for biomass may develop When a local biomass market is established, additional economic activities will be stimulateddspecialized trucking and service providers, financial services, etc However, while considering the benefits of biopower to the local communities, negative impacts are also possible For example, traffic congestion and accidents could increase due to the additional trucks moving the low density biomass feedstock Other possible drawbacks may include unpleasant odors and appearance, diminished property values, health and safety concerns, etc (Gold, 2011) Research into these issues is necessary to weigh the benefits and costs, and to identify strategies that will enhance the benefits while limiting the costs Acknowledgments We thank the subject editor and two anonymous reviewers for their constructive comments regarding the contents and format, which helped us to greatly improve the manuscript X CðnÞ ¼ n X n  4DepðnÞ þ OMðnÞ þ ( X f X ½Delðn; f ; lÞ*Q ðn; f ; lÞ l þ HPðn; f Þ* X Q ðn; f ; lÞ þ Strðn; f Þ*Q s ðn; f Þ l = ; X þ fTaxðn; eÞ*EMðn; eÞg e À ACTðnÞ*CAPðnÞ*365*24*P Constraints: Electricity Supply constraint (electricity supply ! demand): X X ½ACTðnÞ*CapðnÞ*365*24 ! DðnÞ n n Capacity constraint (activity ½RESERVEðnÞ*ACTðnÞ capacity): CAPðnÞ Emission constraint (pollutants within limit): ENVðn; eÞ ENV Limitðn; eÞ Feedstock supply constraint (feedstock supply ! demand): X ½Q ðn; f ; lÞ þ Q s ðn; t; f Þ Supplyðf ; lÞ n Energy content constraint (energy supplied ! energy used): # " X X Q ðn; f ; lÞ ! CAPðnÞ*ActðnÞ*365*24 ENGðn; f Þ* f l Variables and parameters: ACT(n): activity of biopower plant n, i.e., operation percentage; CðnÞ: total cost associated with biopower generation; CAP(n): installed capacity of biopower plant n; Del(n,f,l): feedstock delivery cost, including transportation cost, Tran(n,f,l), and R&H cost, R&H(f,l); Dep(n): annual depreciation of total investment for biopower plant n; EM(n, e): emissions of pollutant e in biopower plant n; Ems(f,n,e): emission coefficients of pollutant e for fuel f at plant n; ENG(n,f): energy content of fuel f ENV(n,e): environmental pollutants, P P i:e:; ½Emsðf ; n; eÞ* Q ðn; f ; lÞ; f cost of handling l HPðnÞ: and processing in biopower plant n; OM(n): cost of operation and maintenance in biopower plant n; P: price of electricity; Q ðn; f ; lÞ : decision variable, quantity of fuel f used in biopower plant n, from location l; Q s ðn; f Þ: quantity of fuel f stored in biopower plant n; Strðn; f Þ : cost of storage of fuel f in biopower plant n; Supply(f,l): supply of feedstock f at location l; Tax(n, e): tax or incentives on emission of pollutant e in biopower plant n Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 531 Appendix B Results of 30 scenarios with various transportation and R&H costs Baseline (Liu, et al., 2014) TC þ 10% TC À 10% R&H þ 10% R&H À 10% Biomass Co-firing level 10% biomass co-firing level Biomass availability 10% 20% 30% 10% 20% 30% Biomass-fired capacity (MW) 85.42 85.42 85.42 128.13 128.13 128.13 8111 147,508 11,979 1528 38,183 193,023 17,821 2112 2251 163,921 1244 2484 3578 243,081 5435 2751 1958 163,916 535 3533 3377 245,881 1867 3726 1,171,303 3,068,333 321,893 1,025,040 5,586,569 3,855,384 1,731,185 995,860 2,915,212 321,893 1,025,040 5,258,005 3,855,384 1,402,620 909,584 2,899,286 321,893 1,025,040 5,155,804 3,855,384 1,300,419 1,678,171 4,397,311 321,893 1,537,560 7,934,935 5,783,076 2,151,859 2,026,277 4,509,496 321,893 1,537,560 8,395,226 5,783,076 2,612,149 1,493,789 4,358,181 321,893 1,537,560 7,711,423 5,783,076 1,928,347 8111 152,002 12,043 1562 38,183 192,956 17,821 2179 2550 163,342 1445 2521 3698 240,626 4083 2822 3267 161,964 994 3533 3825 245,029 2168 3782 1,331,938 3,163,214 321,893 1,025,040 5,842,085 3,855,384 1,986,701 1,092,229 2,908,423 321,893 1,025,040 5,347,585 3,855,384 1,492,201 986,460 2,898,545 321,893 1,025,040 5,231,939 3,855,384 1,376,554 1,828,435 4,397,192 321,893 1,537,560 8,085,081 5,783,076 2,302,004 2,228,720 4,509,496 321,893 1,537,560 8,597,669 5,783,076 2,814,593 1,638,355 4,362,644 321,893 1,537,560 7,860,452 5,783,076 2,077,376 8111 152,135 12,043 1429 38,183 193,023 17,821 2112 2178 165,082 533 2118 2871 245,812 3489 2751 1679 164,430 338 3533 3267 247,668 849 3081 1,054,336 3,163,136 321,893 1,025,040 5,354,406 3,855,384 1,709,022 902,267 2,899,574 321,893 1,025,040 5,148,775 3,855,384 1,293,391 821,277 2,896,531 321,893 1,025,040 5,064,742 3,855,384 1,209,357 1,517,130 4,390,757 321,893 1,537,560 7,767,340 5,783,076 1,984,264 1,823,649 4,509,496 321,893 1,537,560 8,192,598 5,783,076 2,409,521 1,351,976 4,350,996 321,893 1,537,560 7,562,424 5,783,076 1,779,348 8111 147,653 11,979 1384 38,183 193,023 17,821 2112 2178 165,048 566 2118 2871 245,662 3005 2766 1679 164,430 338 3533 3267 247,428 994 3177 1,171,303 3,375,167 321,893 1,025,040 5,893,403 3,855,384 2,038,018 1,001,411 3,190,730 321,893 1,025,040 5,539,075 3,855,384 1,683,690 912,531 3,167,218 321,893 1,025,040 5,426,681 3,855,384 1,571,297 1,693,223 4,802,360 321,893 1,537,560 8,355,036 5,783,076 2,571,960 2,026,277 4,960,445 321,893 1,537,560 8,846,175 5,783,076 3,063,099 1,501,149 4,785,376 321,893 1,537,560 8,145,978 5,783,076 2,362,902 8111 147,442 11,979 1595 38,183 193,023 17,821 2112 2251 163,884 1244 2521 3698 240,522 7800 2807 3267 161,776 1182 3533 3377 245,660 1867 3947 1,171,303 2,761,500 321,893 1,025,040 5,279,736 3,855,384 1,424,352 995,850 2,623,700 321,893 1,025,040 4,966,483 3,855,384 1,111,098 898,418 2,712,458 321,893 1,025,040 4,957,809 3,855,384 1,102,425 1,676,825 3,958,894 321,893 1,537,560 7,495,172 5,783,076 1,712,096 2,026,277 4,058,546 321,893 1,537,560 7,944,276 5,783,076 2,161,200 1,493,626 3,922,523 321,893 1,537,560 7,275,602 5,783,076 1,492,526 Biomass Feedstock Required Mill Residue (tons) Corn Stover (tons) Wheat Straw (tons) Sorghum Straw (tons) Costs Transportation Cost ($) Harvesting/Resource Cost ($) Handling & Processing Cost ($) O&M cost ($) Total Cost ($) Saved Cost of buying Coal ($) Extra Cost of Using Biomass ($) Biomass Stock Required Mill Residue (tons) Corn Stover (tons) Wheat Straw (tons) Sorghum Straw (tons) Costs Transportation Cost ($) Harvesting/Resource Cost ($) Handling & Processing Cost ($) O&M cost ($) Total Cost ($) Saved Cost of buying Coal ($) Extra Cost of Using Biomass ($) Biomass Stock Required Mill Residue (tons) Corn Stover (tons) Wheat Straw (tons) Sorghum Straw (tons) Costs Transportation Cost ($) Harvesting/Resource Cost ($) Handling & Processing Cost ($) O&M cost ($) Total Cost ($) Saved Cost of buying Coal ($) Extra Cost of Using Biomass ($) Biomass Stock Required Mill Residue (tons) Corn Stover (tons) Wheat Straw (tons) Sorghum Straw (tons) Costs Transportation Cost ($) Harvesting/Resource Cost ($) Handling & Processing Cost ($) O&M cost ($) Total Cost ($) Saved Cost of buying Coal ($) Extra Cost of Using Biomass ($) Biomass Stock Required Mill Residue (tons) Corn Stover (tons) Wheat Straw (tons) Sorghum Straw (tons) Costs Transportation Cost ($) Harvesting/Resource Cost ($) Handling & Processing Cost ($) O&M cost ($) Total Cost ($) Saved Cost of buying Coal ($) Extra Cost of Using Biomass ($) 15% biomass co-firing level 532 Z Liu et al / Journal of Cleaner Production 135 (2016) 523e532 References Akhtari, S., Sowlati, T., Day, K., 2014 Economic feasibility of utilizing forest biomass in district energy systems e a review Renew Sust Energ Rev 33, 117e127 Altman, I.J., Johnson, T.G., Badger, P.C., Orr, S.J., 2007 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