- freely available
Sustainability 2014, 6(2), 571-588; doi:10.3390/su6020571
Published: 27 January 2014
Abstract: One of the most important concerns facing Taiwan is lack of energy security. The study examines to what extent the Taiwan energy security can be enhanced through bioenergy production and how bioenergy affects net greenhouse gases emissions. Ethanol, conventional bioelectricity and pyrolysis based electricity are analyzed and emissions from fertilizer use and land use change are also incorporated. The study employs the Modified Taiwan Agricultural Sector Model (MTASM) for economic and environmental analysis. The results indicate that Taiwan indeed increases its energy security from bioenergy production but net greenhouse gases emissions are also increased. Emissions from fertilizer use and land use change have significant impacts on emissions reduction and pyrolysis does not always provide net greenhouse emissions offset. Some policy implications including goal determination, land availability and emissions trading systems are also provided for potential policy decision making.
Taiwan is vulnerable to high energy prices and market distortions in the world energy market because only a small fossil fuel stock is found in Taiwan and most of Taiwan’s energy is imported . To enhance Taiwan’s energy security, there is interest for the Taiwanese to produce energy on its own. In addition to the energy insecurity, another serious challenge facing Taiwan is climate change. According to the 2007 report by the Intergovernmental Panel on Climate Change , the Earth is warming due to anthropogenic emissions of greenhouse gases (GHGs) and its temperature is very likely to increase in the next decades. Such warming would have consequences ranging from increased desertification, a rise in the ocean level to the possible increased occurrences of hurricanes, which may bring potential significant damages to Taiwan. As the 25th largest CO2 emissions country , Taiwan is willing to reduce CO2 emissions and mitigate global climate shift to avoid unwelcome climate impacts, once the energy security issue is resolved. Renewable energy sources that can potentially substitute fossil fuels and provide some of the domestic energy supply include wind and solar energy, hydro-power, geothermal energy and bio-energy . Among these renewable energy alternatives, Taiwan has been developing bioenergy for several years. Geographically, Taiwan’s land area is about 14,000 square miles with 67% of that land being mountainous. Land is a scarce resource in Taiwan and has been intensively utilized in various ways. From this point of view, Taiwan would not be able to produce bioenergy because a substantial amount of land is required for bioenergy production. However, participation in the World Trade Organization (WTO) offers a possibility of development of bioenergy in Taiwan because Taiwan’s agricultural sector is less competitive and part of Taiwan’s agricultural land has been idle. Net idled cropland has increased from 68,000 hectares to 280,000 hectares, which provides a potential stock of land for bioenergy feedstock production .
Although bioenergy can potentially enhance Taiwan’s energy security and reduce GHG emissions [5,6], two important factors, the GHG emissions from land use change and fertilizer use, have been ignored. When agricultural land is converted into other uses, NOx emissions will change and result in different CO2 equivalent (CO2e hereafter) emissions [7,8,9]. If the change in NOx is small, neglecting to consider this factor may not significantly affect the result. However, this change is usually large [10,11]. Snyder et al.  also point out that the most important GHG issue from agriculture is N2O, mainly from soils and N inputs to crop and soil systems. They show that, from the global warming potential (GWP) point of view, even though N2O is a small part of the overall GHG issue, agriculture is considered to be the main source that is linked to soil management and fertilizer use. Therefore, examining bioenergy production and GHG emissions offset without considering associated GHG emissions from land use change and fertilizer use may result in the disaster. This study aims to examine the GHG emissions from various bioenergy production levels under different gasoline, coal and GHG prices. The work makes contributions by integrating multiple bioenergy technologies (ethanol, co-fire and pyrolysis), energy crops, energy and GHG prices and emissions from land use change and fertilizer use into a single study, which provides information about potential enhancement of Taiwan’s energy security and GHG emissions offset to the Taiwanese government.
2. Literature Review
Among available bioenergy techniques, Taiwan can produce bioenergy in the forms of ethanol, direct combustion biopower (conventional bioelectricity) and biopower through pyrolysis (pyrolysis-based electricity). Because these technologies are not mutually exclusive and can be employed at the same time, it is necessary for us to consider all combinations. Bioenergy involving ethanol and conventional electricity have been examined and applied for more than a decade, but bioenergy produced from pyrolysis is intensively studied only in recent years. Pyrolysis involves heating biomass in the absence of oxygen and results in the decomposition of biomass into biooil, biogas and biochar. Biooil and biogas are used to generate electricity in the pyrolysis plant while biochar was also used as an energy source but many studies found that biochar can bring significant environmental and associated economic benefits when it is used as a soil amendment [13,14,15,16,17]. In general, pyrolysis can be categorized as fast pyrolysis, medium pyrolysis, slow pyrolysis and gasification. In this study, we examine the two popular types of pyrolysis techniques (fast and slow pyrolysis), and two uses of biochar (burn biochar in the pyrolysis plant or haul biochar back to the cropland) are incorporated in our bioenergy production framework.
The reason that we would like to examine biochar is because it has been shown to improve agricultural productivity and the environment in several ways. Specifically, biochar is stable in the soil  and has nutrient-retention properties that lead to increases in crop yields . Moreover, biochar offers a chance to sequester carbon . As pyrolysis can provide a significant amount of renewable energy and offset more GHG emissions [5,6,16], it is a potential bioenergy technique that Taiwan is interested in.
Lifecycle analysis of GHG emissions has been examined widely in bioenergy production [7,13,16,19] and land use changes [10,11,20,21,22]. Some studies examined the impacts of emissions from global land use changes on the lifecycle emissions of corn ethanol [7,9] and other studies focused on the land use change emissions when specific land types are cultivated for cropland use [22,23,24]. Land use change has also been examined in large scale. For example, Timilsina et al.  analyzed the long-term impacts of large-scale expansion of biofuels on land use change, food supply and prices, and the overall economy in various countries or regions, while Kwon et al.  examined the state-level soil carbon emissions for direct land use change in the United States. Land use change also has various GHG effects. Schaufler et al.  found that different land-use strongly affected GHG fluxes in cropland, grassland, forests and wetland. N2O and CO2 emissions are highest in grassland soils while NO emissions are highest in forest soils, which are also positively correlated with N input. Moreover, Baldos  utilized the Land Use Change Emissions (LUCE) modules and found that the direct lifecycle GHG emissions of corn ethanol fuel can exceed the 20% GHG reduction requirement in the US EISA given the data and assumptions during corn farming and ethanol production. Wang et al.  also developed a widely applied modeling approach (including GREET model and CCLUB model (Carbon Calculator for Land Use Change from Biofuels Production)) in the US to study biofuel life cycle. Baggs et al.  found that zero tillage resulted in higher N2O emissions than conventional tillage and N2O emissions were generally correlated with CO2 emissions. However, this result must be adopted very carefully when different land types are examined. Grover et al.  indicated that soil-based GHG emissions will increase from 53 to 70 t CO2-equivalents after land use change. Farquharson and Baldock  indicated that adding N fertilizers will increase N2O emissions due to nitrification and denitrification process where the ammonium (NH4+) is available for nitrification. Synder et al.  also showed that fertilizer induced N2O emissions from soil equates to a GWP of 4.65 kg CO2 kg−1 of N applied. Therefore, when we change crops and associated N fertilizer application, it will lead to different NOx emissions. Qin et al.  developed an agroecosystem model (AgTEM) that was incorporated with biogeochemical and ecophysiological processes. They found that N2O emitted from croplands with high N application rates is mostly larger than those with lower N input levels. The average N2O flux is 1.8 kg N ha−1 and most of the simulation results are within a reasonable range (e.g., less than 5 kg N ha−1). Their recent work  also examined carbon and nitrogen dynamics for maize and cellulosic crops. They indicated that for maize the global warming potential (GWP) amounts to 1–2 Mg CO2eq ha−1 yr−1, with a dominant contribution of over 90% from N2O emissions while cellulosic crops contribute to the GWP of less than 0.3 Mg CO2eq ha−1 yr−1. The general process of NOx emissions is depicted as Figure 1.
However, they also point out that this estimate will vary depending on local climates since NH3 volatilization and NO3− leaching are heavily affected by the climate. These studies focus on how many GHG emissions are produced during bioenergy production or the mechanism of GHG emissions from land use change, all of which have provided significant contributions to the literature, but the relationship between bioenergy production strategies and GHG emissions from land use change are usually not linked. Therefore, in order to know how land use GHG emissions may affect the benefits from bioenergy production, it is necessary to incorporate the emissions from fertilizer and change on land use into the bioenergy production to present a more general lifecycle analysis on bioenergy.
3. Model Structure
The model used herein is based on price endogenous mathematical programming, which is originally illustrated by Samuelson . Samuelson shows the equilibrium in the perfect competition market can be derived from the optimization model that maximizes the consumer surplus and producer surplus. Takayama and Judge  establish a mathematical programming model on spatial model based on Samuelson’s idea while McCarl and Spreen  point out that this model is useful in policy analysis, especially in its property of price endogeneity. In addition, McCarl and Spreen  compare the linear programming models used by other planned economic systems to the price endogenous model, and the results showed that the price endogenous model can represent the economic system in a perfectly competitive market and thus, can be useful in policy analysis including soil conservation policy , global climate change [34,35,36], and climate change mitigation . It has also been applied extensively for research evaluation [38,39].
Chen and Chang  develop the Taiwan Agricultural Sector Model (TASM) to analyze the Taiwanese agricultural policy in terms of production and market issues. The TASM is a multi-product partial equilibrium model based on the previous work [32,33,39,41]. This empirical structure has been adapted to Taiwan and used in many policy-related studies [40,42,43]. The current version of TASM accommodates more than 110 commodities in 15 subregions aggregated into four major production and processing regions. We extended the TASM to evaluate the potential economic and GHG implications of bioenergy crop production plus competition with other land uses. GHG emissions from land and fertilizer use are also incorporated into the modified TASM. The modified TASM simulates market operations under assumptions of perfect competition with individual producers and consumers as price-taker. It also incorporates price-dependent product demand and input supply curves.
3.1. Modified Taiwan Agricultural Sector Model
TASM was constructed by Chen and Chang  under above theory and for this analysis we extend this model by adding features related to bioenergy and N2O emissions. Specifically, to get a version for use herein, we have to address how energy crops and GHG emissions are incorporated in the modified TASM. We illustrate the algebraic form of the objective function of the modified TASM and its constraints. The objective function and constraints of modified TASM are shown as follows:
Table 1 details the variables using in the objective function and constraints.
|Table 1. Variables.|
|Variable||Description of Variables|
|Qi||Domestic demand of ith product|
|Government purchases quantity for price supported ith product|
|Import quantity of ith product|
|Export quantity of ith product|
|ψ(Qi)||Inverse demand function of ith product|
|Government purchase price on ith product|
|Cik||Purchased input cost in kth region for producing ith product|
|Xik||Land used for ith commodities in kth region|
|Lk||Land supply in kth region|
|αk(Lk )||Land inverse supply in kth region|
|Rk||Labor supply in kth region|
|βk(Rk )||Labor inverse supply in kth region|
|ALk||Set-aside acreage in kth region|
|SUBj||Subsidy on planting jth energy crop|
|ECjk||Planted acreage of jth energy crop in kth region|
|Inverse excess import demand curve for ith product|
|Inverse excess export supply curve for ith product|
|TRQi||Import quantity exceeding the quota for ith product|
|EXED(TRQi)||Inverse excess demand curve of ith product that the import quantity is exceeding quota.|
|taxi||Import tariff for ith product|
|outtaxi||Out-of-quota tariff for ith product|
|Yik||Per hectare yield of ith commodity produced in kth region|
|Egik||gth greenhouse gas emission from ith product in kth region|
|PGHG||Price of GHG gas|
|GWPg||Global warming potential of gth greenhouse gas|
|GHGg||Net greenhouse gas emissions of gth gas|
|Baselineg||Greenhouse gas emissions under the baseline of the gth gas|
|fik||Labor required per hectare of commodity i in region k|
The objective function of the modified TASM model incorporates the domestic and trade policies where the first term is the area under the domestic demand curve and the second, third and fourth terms stand for input costs, cropland rent and labor costs, respectively. The fifth, sixth and seventh terms reflect the government subsidy on rice purchase, set-aside lands and for planting energy crops to represent the social welfare in a closed market. The eighth and ninth terms represent the area under the excess demand curve and the 10th term stands for the area under the excess supply curve. The 11th term is tariff revenue. GHG emission is modified in the last term to reflect that GHG emissions reduce social welfare. Equation (2) is the balance constraint for commodities. Equations (3) and (4) are the resource endowment constraints. Equation (3) controls cropland and Equation (4) is the other resource constraint. Equation (5) is further modified to reflect the greenhouse gas balance which shows emissions emitted of CO2e (including emissions from bioenergy production, land use change and fertilizer use but CH4 emissions from animal manures) cannot be greater than total emissions.
The data sources of agricultural commodities largely come from published government statistics and research reports, which include the Taiwan Agricultural Yearbook, Production Cost and Income of Farm Products Statistics, Commodity Price Statistics Monthly, Taiwan Agricultural Prices and Costs Monthly, Taiwan Area Agricultural Products Wholesale Market Yearbook, Trade Statistics of the Inspectorate-General of Customs, Forestry Statistics of Taiwan. Demand elasticities of agricultural products come from various sources and were gathered and sent by Chang and Chen.
4. Study Setup
This study examines Taiwan’s bioenergy production from ethanol, conventional bioelectricity and pyrolysis based electricity, and GHG emissions offset by utilizing current set-aside land with the consideration of the emissions from fertilizer use and land use change. Three gasoline prices (NT$20, 30, 40 per liter), two coal prices (NT$1.7, 3.45 per kg), six GHG prices (NT$ 5, 10, 15, 20, 25, 30 per ton) plus estimated emissions from fertilizer use and land use change. The simulated gasoline and coal prices are selected based on the ranges of their market prices in 2012. Since Taiwan has not established a GHG trading mechanism and GHG emission is currently of no value in Taiwan, the study examines several potential GHG prices based on the opinion of Professor Chi-Chung Chen, who is familiar with and engaged in Taiwanese agricultural and environmental policies.
The net mitigation of CO2 from ethanol is estimated by . They show that net CO2 emissions are reduced by 0.107 ton per 1000 liters of ethanol. For conventional electricity, McCarl  shows that poplar can offset about 71.3% of carbon dioxide emissions relative to the fossil fuel and 75.1% for switchgrass. We calculate that the emissions reduction is 0.195 kg CO2 for poplar and 0.246 kg CO2 for switchgrass.
GHG emissions from land use change are estimated by Liu et al. , who calculate that annual mean GHG fluxes from soil of plantation and orchard are 4.70 and 14.72 Mg CO2-C ha−1 yr−1, −2.57 and −2.61 kg CH4-C ha−1 yr−1 and 3.03 and 8.64 kg N2O-N ha−1 yr−1, respectively. Qin et al.  also indicated that the average N2O flux is 1.8 kg N ha−1 and most of the simulation results are less than 5 kg N ha−1. Because CO2 and N2O emissions are highly correlated with each other , we assume that the emission profile of CO2 and N2O are staying at the same level. In addition, Snyder et al.  show that fertilizer induced N2O emissions from soil equates to a GWP of 4.65 kg CO2 kg–1 of N applied. With these estimates, we arrive at the estimated emission level from fertilizer use and land use change (Table 2). Biochar also offers GHG emissions offset potential. This study also incorporates the GHG effect for different uses of biochar to see how it affects the GHG emissions reduction, based on Kung et al.’s estimates .
|Table 2. Estimated emission level from fertilizer use and land use change.|
|GHG||Units||Estimated emission level|
|GHG emissions from fertilizer and land use change||CO2||Mg ha−1 yr−1||4.7|
|CH4||kg ha−1 yr−1||−2.57|
|N2O||kg ha−1 yr−1||26.86|
|Net emissions||CO2e||Mg ha−1 yr−1||11.62|
5. Results, Policy Implications
The simulation result indicates that when Taiwan tries to enhance its energy security by developing bioenergy, net GHG emissions are likely to increase, especially when GHG price is low (see Figure 2 and Figure 3). As indicated in Figure 2, emissions reduction from Taiwan’s bioenergy production is lower than the emissions increased from fertilizer use and land use change. Only when GHG price is high and gasoline price is low, net emissions reduction may be achieved, and when the gasoline price keeps increasing, net emissions will increase (Figure 3).
The result shows that when energy security is the first priority of Taiwanese government, net GHG emissions will not be reduced in most cases. In other words, the study indicates that Taiwan gains energy security at a cost of emitting more. This is partly due to the fact that the emission offset ability of ethanol is lower than that of pyrolysis based electricity. When the gasoline price is low, feedstocks will be used in ethanol and electricity production but when the gasoline price increases, more feedstocks are converted into the ethanol production and the total amount of emissions offset is reduced. Market price also affects the net emissions from fertilizer use and land use change. Under low energy prices, bioenergy production is less profitable and less set-aside land is used for energy crop plantation. Fewer plantations require fewer fertilizer and therefore, emissions from fertilizer use and land use change will be smaller. When energy prices increase, more land is converted and brings higher emissions from land use change and fertilizer use. Although bioenergy is considered as a carbon sequestration technology, lifecycle analysis including fertilizer use and land use change indicates that ethanol does not bring GHG emissions reduction while pyrolysis is possible to offset emissions under certain conditions.
In this study, bioenergy comes from various sources including ethanol, conventional bioelectricity and pyrolysis based electricity. The result indicates that conventional bioelectricity is driven out by pyrolysis based electricity and electricity is solely produced via pyrolysis. In general, pyrolysis produces three outputs including biooil, biogas and biochar, all of which can be used to generate electricity. Because biochar is found to enhance crop yield and store carbon in a more stable form when used as a soil amendment, various uses of biochar are incorporated into the study. The result indicates that when biochar is used as a soil amendment, bioenergy production is relatively lower than when biochar is burned in the pyrolysis plant (Figure 4,Figure 5 and Appendix). However, if biochar is burned to provide electricity, it is unlikely to provide net GHG emissions offset. Using biochar as a soil amendment is possible to offset GHG emissions only when the GHG price is high. If the GHG price is low, ethanol production is high and fast pyrolysis that will generate more electricity will dominate slow pyrolysis that yields more biochar. Only when the GHG price increases to a certain level, slow pyrolysis becomes a dominant technology and ethanol production decreases.
The study provides some insights about environmental effects on Taiwan’s energy security concern via bioenergy production. The result shows that Taiwan’s energy security can be enhanced by producing ethanol and electricity using currently set-aside land. However, bioenergy does not bring environmental benefits in most of cases when land GHG and fertilizer emissions are incorporated. In other words, bioenergy does not always offset GHG emissions in a broader lifecycle analysis. Some policy implications for Taiwan to gain economic and environmental benefits plus enhancement of energy security are provided including:
When Taiwan tries to develop a GHG emissions trading mechanism, effects of the trading system on domestic renewable energy production must be incorporated. As the study shows, bioenergy production is heavily impacted by GHG prices. Therefore, under a marketable GHG emissions trading system, effectiveness of energy security enhancement from bioenergy must be validated;
Development of the bioenergy industry requires long term planning. The simulation result indicates that Taiwan can enhance energy security from bioenergy production at a cost of higher emissions. However, under low energy prices, less set-aside land will be converted into the energy crop plantation and results in a net emissions offset. Bioenergy production will shrink under this situation. Therefore, in order to ensure energy security enhancement when the energy price is low, some government subsidies may be required for farmers to convert set-aside land into energy crop plantations;
This study shows that GHG emissions from fertilizer use and land use change are significant and have important impacts on both bioenergy production and net GHG emissions offset. Therefore, a proper estimation of these emission rates is required. The study examines the bioenergy production and GHG effects on Taiwan’s set-aside land, located in the four major areas in Taiwan. Due to local soil and weather conditions, NOx emission rates from land use change and fertilizer application should not be the same in these areas and future studies must be conducted in order to draw a more realistic picture;
Although energy security is the prior concern on Taiwan’s bioenergy development, it may not always be so. As Taiwan is facing direct challenges from global climate shifts, GHG mitigation is another important issue that the Taiwanese must address. Bioenergy is one possible way to increase domestic energy supply, but it may not be an appropriate method for GHG emissions offset, especially for the significant effects from fertilizer and land use change emissions. As the result shows, Taiwan is not able to achieve the maximal bioenergy production and GHG emissions offset at the same time. The Taiwanese government must take this into account for future policy decisions;
Not all set-aside land can be used for bioenergy production. Joining the WTO releases some agricultural land but the Taiwanese government has been trying to utilize the idle land for other economic purposes including development of recreation sites and high economic value commodities. Therefore, using all set-aside land in bioenergy production may not be feasible. Further adjustments combining all existing and potential agricultural and associated policies may be required.
The study examines how much bioenergy can be produced and the consequent GHG emissions effect as Taiwan attempts to enhance its energy security. Simulation results indicate that while bioenergy indeed increases Taiwan’s energy security, it is likely to increase net GHG emissions. This is somewhat contradictory to previous studies showing bioenergy provides both renewable energy and GHG emissions mitigation. Our result shows that emissions reduction by bioenergy is offset by the emissions of fertilizer use and land use change. Throughout 72 scenarios, only eight cases show net GHG emissions offset. GHG price is another important factor influencing the bioenergy production and GHG emissions offset. At a higher GHG price, ethanol production will shrink to a very low level and slow pyrolysis dominates all other bioenergy technologies. However, when Taiwan places energy security as its first priority, the impacts of GHG price on bioenergy production will be small.
The study has some limitations that must be addressed. First, potential uncertainties exist for many important factors. Depending on land types, GHG emissions from land use change, fertilizer use and soil carbon sequestration may differ, all of which lead to a different result. Second, Taiwan’s GHG trading system has not been established and therefore, GHG prices used in this study are only based on the professional opinions rather than real market data. Further investigation is needed when the GHG emissions trading market is built. Third, the hauling distance of biofuel and biochar is estimated from McCarl et al.’s study , which assumes the pyrolysis plant is in the centre of a square surrounded by a grid layout of roads. This assumption may be released by combining GIS method to reflect a more accurate hauling distance and associated GHG emissions. Finally, CH4 is another important GHG in agriculture, especially for rice paddies and animal manure. This study does not incorporate CH4 emissions from specific agricultural commodities; instead, the study focuses on the CH4 emissions from the land used for bioenergy crop plantation. CH4 emissions must be incorporated into the analysis when rice straw and manure are used in bioenergy production (e.g., pyrolysis).
Chih-Chun Kung would like to thank the financial support from the National Natural Science Foundation of China (#41161087; #41061049；#71173095; #71263018), National Social Science Foundation of China (#12&ZD213), China Postdoctoral Foundation (2013M531552) and University Social Science Project of Jiangxi (JJ1208). We also sincerely appreciate the great assistance and valuable comments from Bruce A. McCarl at Texas A&M University and Chi-Chung Chen at National Chung-Hsing University.
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|Table A1. Simulation results of bioenergy production and GHG emissions|
|Term||Unit||Haul biochar to the cropland as soil amendment|
|Total Planted Ha||1000 Ha||113.5||113.5||113.73||105.59||114.14||113.73|
|CO2 Emission Reduction||Tons||603,841||216,557||201,581||587,067||564,761||498,605|
|Emissions (FU & LUC)||Tons||1,318,870||1,318,870||1,321,543||1,226,956||1,326,307||1,321,543|
|Total Planted Ha||1000 Ha||114.34||113.84||114.07||114.36||106.57||114.07|
|CO2 Emission Reduction||Tons||780,618||431,894||243,388||1,076,035||776,024||644,134|
|Emissions (FU & LUC)||Tons||1,328,631||1,322,821||1,325,493||1,328,863||1,238,343||1,325,493|
|Total Planted Ha||1000 Ha||115.06||114.85||115.15||114.97||115.05||106.63|
|CO2 Emission Reduction||Tons||1,163,300||1,165,167||1,168,946||1,163,646||1,166,568||776,957|
|Emissions (FU & LUC)||Tons||1,336,997||1,334,557||1,338,043||1,335,951||1,336,881||1,239,041|
|Total Planted Ha||1000 Ha||115||114.62||116.36||114.89||114.85||115.04|
|CO2 Emission Reduction||Tons||1,336,551||1,158,434||1,196,438||1,337,356||1,166,757||1,169,667|
|Emissions (FU & LUC)||Tons||1,336,300||1,331,884||1,352,103||1,335,022||1,334,557||1,336,765|
|Total Planted Ha||1000 Ha||121.01||114.89||114.92||120.97||120.58||115.02|
|CO2 Emission Reduction||Tons||2,208,412||1,333,697||1,165,068||2,183,588||1,389,625||1,171,517|
|Emissions (FU & LUC)||Tons||1,406,136||1,335,022||1,335,370||1,405,671||1,401,140||1,336,532|
|Total Planted Ha||1000 Ha||121.02||116.94||117.15||117.3||115.24||117.16|
|CO2 Emission Reduction||Tons||2,208,492||1,359,715||1,194,854||2,178,646||1,349,855||1,195,118|
|Emissions (FU & LUC)||Tons||1,406,252||1,358,843||1,361,283||1,363,026||1,339,089||1,361,399|
|Term||Unit||Burn biochar at pyrolysis plant to generate electricity|
|Total Planted Ha||1000 Ha||111.91||117||117||111.91||117||117.11|
|CO2 Emission Reduction||Tons||253,214||108,743||108,806||263,370||259,032||238,785|
|Emissions (FU & LUC)||Tons||1,300,394||1,359,540||1,359,540||1,300,394||1,359,540||1,360,818|
|Total Planted Ha||1000 Ha||112.04||117.01||117.15||112.04||117.01||117.1|
|CO2 Emission Reduction||Tons||299,986||179,831||108,823||289,831||281,377||255,040|
|Emissions (FU & LUC)||Tons||1,301,905||1,359,656||1,361,283||1,301,905||1,359,656||1,360,702|
|Total Planted Ha||1000 Ha||111.77||117.01||117.15||112.11||117.01||117.15|
|CO2 Emission Reduction||Tons||340,569||195,062||108,823||320,325||311,840||277,390|
|Emissions (FU & LUC)||Tons||1,298,767||1,359,656||1,361,283||1,302,718||1,359,656||1,361,283|
|Total Planted Ha||1000 Ha||111.6||117||117.16||111.59||117.01||117.16|
|CO2 Emission Reduction||Tons||340,562||342,305||179,910||340,557||342,305||301,765|
|Emissions (FU & LUC)||Tons||1,296,792||1,359,540||1,361,399||1,296,676||1,359,656||1,361,399|
|Total Planted Ha||1000 Ha||111.61||117||117.16||111.6||117.01||117.16|
|CO2 Emission Reduction||Tons||340,567||342,305||190,064||340,562||342,305||342,383|
|Emissions (FU & LUC)||Tons||1,296,908||1,359,540||1,361,399||1,296,792||1,359,656||1,361,399|
|Total Planted Ha||1000 Ha||111.6||117||117.11||111.6||117.01||117.11|
|CO2 Emission Reduction||Tons||340,562||342,305||208,331||340,562||342,305||342,372|
|Emissions (FU & LUC)||Tons||1,296,792||1,359,540||1,360,818||1,296,792||1,359,656||1,360,818|
Note: FU and LUC stand for fertilizer and land use change, respectively.
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