A stochastic evaluation of economic and environmental effects of Taiwan's biofuel development under climate change

A stochastic evaluation of economic and environmental effects of Taiwan's biofuel development under climate change

Energy 167 (2019) 1051e1064 Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy A stochastic evaluati...

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Energy 167 (2019) 1051e1064

Contents lists available at ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

A stochastic evaluation of economic and environmental effects of Taiwan's biofuel development under climate change Chih-Chun Kung Institute for Advanced Studies in Finance and Economics at Hubei University of Economics, Wuhan, 430205 China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 October 2017 Received in revised form 8 November 2018 Accepted 17 November 2018 Available online 19 November 2018

Biofuel production is of particular interest to Taiwan in the face of energy insecurity and climate change. Since climate-induced impacts such as changes in regional temperature and precipitation will influence crop yields, this study accommodates the estimated yield changes and subsequently develops a twostage stochastic programming with recourse model to investigate the economic and environmental effects of biofuel production. The results indicate that the utilization of sweet potato, along with its byproducts, can result in the maximum amount of ethanol production. At higher levels of gasoline and greenhouse gas (GHG) prices, 441.2 million liters of ethanol can be produced, of which 70 million liters come from utilization of by-products. In general, economic factors such as gasoline and GHG prices have larger impacts on biofuel production than yield changes. However, although biofuel production is relative stable in the face of crop yield change, a shift in cultivars and land-use pattern is likely to occur. We show that the net emission reduction is relatively low compared to Taiwan's total emission and the aggregate value of emission reduction merits more investigation. These concerns, as well as resource reallocation and policy reformulation are also discussed in detail. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Crop yield response Ethanol Emission reduction Mathematical programming Policy analysis

1. Introduction Global climate shift has been long believed to be caused by human activities, especially by the intensive use of fossil fuels that leads to enormous amount of greenhouse gas (GHG) emissions. According to the Intergovernmental Panel on Climate Change (IPCC)'s 2007 report, the average temperature has been increasing by approximately 0.5  C in the 20th century and McCarl [1] shows that the global temperature can further increase by up to 1.4  C to 5.8  C by the end of the 21st century. Since climate change can result in serious adverse effects such as increased desertification, a rise in the ocean level, and a possible increase in occurrence of hurricanes [2e5], it is important to discover clean and renewable energy to ensure the sustainable development of society. Taiwan is a tiny island with little natural resources. More than 97% of Taiwan's energy must be imported and this makes Taiwan vulnerable to high energy prices and distortions in the world energy market. Therefore, to improve Taiwan's energy security, domestic production of renewable energy can be of particular interest. Bioenergy is an attractive option because approximately one-third

E-mail address: [email protected] https://doi.org/10.1016/j.energy.2018.11.064 0360-5442/© 2018 Elsevier Ltd. All rights reserved.

or 280,000 ha (ha) of Taiwan's cropland has been set aside due to the lower competitive power of Taiwan's agricultural commodities in international agricultural markets. To stabilize income streams of farmers, supporting policies such as the rice repurchase program and the set-aside program have been launched. As a consequence, a substantial amount of cropland has become available for use in energy crop plantation and biofuel development. Since bioenergy production is highly dependent on agricultural activities, and studies have pointed out that climate change would have impacts on agricultural sector [1,6e19], it is necessary to accommodate the potential climate-induced impacts on agriculture to provide a comprehensive biofuel analysis. This study aims to answer how biofuel production benefits the society as a whole. Specifically, this study explores the economic and environmental effects of activities such as ethanol production under various market operations, government expenditure on biofuel development, potential resource allocation and change in land-use, and mitigation of climate change. To accomplish these tasks, the study develops a two-stage stochastic programming with recourse model that includes the estimated changes in crop yield, biofuel promotion policy, and emission components. The study makes several contributions. First, we present the potential influences of climate change on bioenergy development

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in Taiwan. With this information, the government officers can design a more appropriate policy (or reform existing ones) to encourage biofuel production. Second, the study can determine what energy crop(s), along with the utilization of their crop byproducts, can result in optimal biofuel production. Armed with the new knowledge, the policy-makers can then adjust promotion policies. Third, we illustrate the overall agro-economic effects on resource allocation, changes in agricultural activities and land use under climate change. Finally, the aggregate environmental benefits and mitigation effects are presented that can be useful in future environmental studies.

price endogenous, partial equilibrium modeling framework because it is considered to be useful in policy analysis by McCarl and Spreen [49]; who show that the price endogenous model can well represent the economic system in a perfectly competitive market. This modeling framework has also been applied in many environmental and resource analysis including acid rain [50], soil conservation policy [51], biofuels [52], global climate change [36,53e55], ozone [56], climate change mitigation [25,57], and policy evaluation [51,58].

2. Literature review

To investigate effects of agricultural policy on production and markets, Chen and Chang [59] develop the Taiwan Agricultural Sector Model (TASM). Their empirical structure has been used to analyze various agricultural, bioenergy, transportation, and climatic studies [8,35,44,59,60]). TASM adopts the assumption of perfect competition to simulate market equilibrium. Individual producers and consumers are assumed to be price-takers. It also includes price-dependent product demand and input supply curves. More than 102 commodities (both primary and secondary) are accommodated in the current version of TASM. The total value of these primary commodities embedded in this model accounts for more than 85% of Taiwan's total agricultural product value. Subregional production activities of each commodity and crop and livestock mix activities are specified at the sub-regional level. The input markets such as land types (cropland, pasture land, forest land) and farm labor are specified at the regional level.

Switching to low-carbon fuels such as solar energy, wind power, hydropower, and bioenergy is of high priority to mitigate climate change [20]. Since development of bioenergy is highly dependent on the agricultural activities, it has been intensively studied in countries and regions that have substantial amount of cropland such as the United States, European Union, and Brazil [11,21e28]. Conventionally, bioenergy is produced by agricultural commodities such as corn, sugarcane, soybean, and oilseeds [25], but use of these crops inevitably decreases the world food supply, drives up the food price, and consequently decreases social welfare in countries that are not very rich [29]. Therefore, the secondgeneration bioenergy that uses crop residuals as primary bioenergy feedstocks should be promoted. Campiche et al. [22] point out that as innovations in conversion technology take place, production of cellulosic ethanol can be increased substantially while reducing the production cost. McCarl and Schneider [25] conduct an economic analysis and show that farmers get benefits by selling their corn stover after the introduction of cellulosic technology. A number of studies also suggest that using crop by-products as potential feedstocks for biofuel production can be beneficial [30,31]. Although bioenergy is a means to provide sustainable energy source, opinions vary on whether the use of bioenergy eventually decreases the net CO2 emissions [20,32,33]. Searchinger et al. [32] indicate that CO2 emission may eventually increase if deforestation and a sudden major shift in land use occur, and Field and Campbell [34] point out that the effects of bioenergy on climate depend on the crop selection, the technology adopted, and reflectance of solar radiation between energy crops and pre-existing plants. Since successful bioenergy development relies on stable supply of inputs, climate-induced impacts on changes in crop yield, and consequent effectiveness of bioenergy production, should be taken into account. Many studies have examined the agro-economic effects resulting from climate change [1,7e9,35e37], typical crop yield response and climate change [9,38e43], and bioenergy potential under historical crop yields [1,44e47], but they do not explicitly focus on the production of bioenergy and its economic and environmental impacts under various crop yield changes, or specify how net bioenergy production will be influenced by climate change in a specific country. Failure to aggregate these issues is very likely to result in unrealizable conclusions and less useful policy recommendations. 3. Methodology

3.1. Basics of TASM

3.2. Modeling uncertainty of crop yields into TASM To reflect uncertain climate-induced crop yield responses, we extend TASM to the stochastic programming with recourse (SPR) formulation as discussed in Lambert et al. [61] and Chen and McCarl [62]. The STASM contains more than one state of nature to define crop yield variability, but commodity demand remains the same under each state of nature. Farmers are assumed to make planting decisions before actual yields are revealed. To reflect the variability associated with planting decisions, revenue under each state of nature is then incorporated into the objective function. With this assumption a two-stage approach is embedded where farmers make decisions on crop planting acreages in the first stage while supply and demand balance constraints that ensure market clearance at each state of nature are specified in the second stage. Algebraically the stochastic version of TASM is depicted as equations (1)e(5):

Max W ¼

( Xð Xð rðsÞ* jðQis ÞdQis  ak ðLk ÞdLk

X s





i

k

XX X ð  M M ED Q is dQ is bk ðRk ÞdRk  Cik Xik þ i

k

þ



EXEDðTRQis ÞdTRQis 

i



i

  ES Q Xi dQ Xi

i

) i Xh X G taxi *Q M þ þ outtax *TRQ PG þ i is is i *Q i i

To explore bioenergy potential and associated economic and environmental consequences under climate impacts, we extend the conventional Taiwan Agricultural Sector Model (TASM) to a stochastic programming with recourse version, whose theoretical background is derived from Samuelson [48] and a number of subsequent studies [1,6,35]. Our model is based on a multi-product

k

þ

i

X X X P L *ALk þ SUBj *ALj  PGHG * GWPg *GHGg k

j

g

(1)

C.-C. Kung / Energy 167 (2019) 1051e1064

Subject to

Qis þ Q Xis þ Q G i 

  X Yiks *ð1 þ CCYIELDi Þ*Xik  Q M is þ TRQis k

0

ci; s (2)

X Xik þ ALk  Lk  0

ck

3.3. Study setup

X fik Xik  Rk  0

ck

(4)

i

X Egik Xik  GHGg  0

cg

(5)

i;k

where

QG i QM i Q Xi

uðQi Þ PG i Cik Xik Lk ak ðLk Þ Rk bk ðRk Þ PL ALk EDðQ M i Þ

combinations of crop mixes. Finally, all geographic regions are separated into 4 major processing and production regions, allowing us to examine the economic and environmental effects in whole or in part. However, since this partial equilibrium model assumes the supply and prices of foreign agricultural commodities are exogenous and does not incorporate the changes of agricultural activities outside study region, this framework is generally appropriate for small countries or regions such as Singapore, Hong Kong, and Taiwan.

(3)

i

s Qi

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State of nature s Domestic demand of ith product Government purchases quantity for price supported ith product Import quantity of ith product Export quantity of ith product Inverse demand function of ith product Government purchase price on ith product Purchased input cost in kth region for producing ith product Land used for ith commodities in kth region Land supply in kth region Land inverse supply in kth region Labor supply in kth region Labor inverse supply in kth region Set-aside subsidy Set-aside acreage in kth region Inverse excess import demand curve for ith product

Inverse excess export supply curve for ith product ESðQ Xi Þ Import quantity exceeding the quota for ith product TRQi EXEDðTRQi Þ 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 (ha) yield of ith commodity produced in kth region fikYik Labor required per hectare of commodity i in region k fik Labor required per hectare of commodity i in region k SUBj Subsidy on planting jth energycrop ECjk Planted acreage of jth energy crop in kth region Price of GHG gas PGHG GWPg Global warming potential of gth greenhouse gas Net greenhouse gas emissions of gth gas GHGg gth greenhouse gas emission from ith product in kth region Egik

Equation (1) is the objective function that represents the domestic and international trade policies. Equation (2) is a balance constraint indicating that the quantity of commodity sold is not excess the quantity of commodity produced. Equations (3) and (4) control the use of cropland and other resources. Equation (5) ensures that the net emission reduction cannot exceed total emission. Strengths associated with this formulation merit more discussion. First, the commodity coverage is representative because most of agricultural activities are included. Second, historical production patterns are included to prevent from having unrealistic

This study aims to simulate the economic and environmental effects of biofuel development in Taiwan under climate impacts, it incorporates two types of estimates and projections of potential crop yield changes used by Chang et al. [35] to examine how these factors may influence Taiwan's bioenergy production. Two climate change projections are adopted where the projection A assumes a 1% increase in temperature with a 6% increase in precipitation while projection B assumes a 6% increase in temperature with a 9% increase in precipitation. Economic factors such as gasoline prices and GHG prices are also incorporated. Since set-aside land is distributed across Taiwan, the most important selection criterion is that the potential energy crop must be suitable for all areas with relatively stable and greater yields. In Table 1 we compare several energy crops and we find that the sweet potato appears to be a better choice than sugarcane, corn, and sweet sorghum for large scale production of bioenergy and for the efficient use of aside land across Taiwan. In addition to conventional energy crop such as sweet potato, the potential use of woody biomasses such as willow, poplar, and switchgrass are also suggested by the Ministry of Agriculture because they can be used either to produce bioenergy or sequester carbon (similar to reforestation), both of which make economic and environmental benefits. Since these woody biomasses may be planted for multiple purposes, we add them as other alternatives to see whether or not they may involve in ethanol production (or they should be used in reforestation). After all potential energy crops have been selected, we provide Table 2 to presents their characteristics such as energy conversion rates and offset reduction potential. Based on the 10 billion liters of gasoline consumed per year, Taiwanese government plans to develop E3-gasoline (gasoline with 3% of ethanol content) because it is an attainable goal in the face of limited land availability. To meet the ethanol demand, 300 million liters of ethanol must, thus, be produced. Therefore, this study will focus on whether this demand may be satisfied under various climate change and market conditions. The range of gasoline prices is based on Taiwan's historical trading prices of petroleum while the value of emission reduction is taken from the Chicago Climate Exchange. The units of gasoline and emission price are set as NT$1 per liter and NT$ per ton, respectively. To investigate the usefulness of crop wastes, the biofuel feedstocks include sweet potato, willow, poplar, switchgrass, and their by-products. The processing cost shown in Table 2 is an aggregate value that includes the annualized plant operation costs, input processing costs, and transportation costs. The processing costs and hauling costs associated with primary and secondary agricultural commodities are then slightly modified to reflect the potential climate-induced impacts on agricultural activities.

1 New Taiwan dollar (NT$) is used throughout this study. For readers' convenience, the 6-month average exchange rates of per USD ($), Euro (V), and Pound (£) to NT$ are NT$30.31, NT$35.39, and NT$40.31.

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C.-C. Kung / Energy 167 (2019) 1051e1064

4. Results

Table 1 Production region of potential bioenergy feedstocks. Crops

Planted regions county

Sweet Potato

Taipei, Taoyuan, Hsinchu, Miaoli, Taichung, Nantu, Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, Ilan, Pingtung, Hualian, Taitung (Area: all counties of Taiwan) Sweet Sorghum Yunlin, Chiayi, Tainan Sugarcane Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, Pingtung Corn Hsinchu, Miaoli, Pingtung, Taitung, Tainan, Chiayi, Yunlin

Harvest

Crop yielda

times/ year

ton/ hectare year

3

37.5

Food, animal feedstock, secondary commodity

1 1

4.05 26.65

Liquor Cane sugar

2

11.72

Food, animal feedstock, secondary commodity

Major uses

Source: Taiwan Agricultural Yearbook and (a) [44].

Table 2 Information of feedstock choices. Crops

Sweet Potato Willow Poplar Switchgrass

Processing cost

Conversion Rate

GHG Reduction

Average Output

$NT/kg of feedstock

% of feedstock

ton/ton of feedstock

ton ha1 yr1

7.6a

12.50%a

0.014a

37.51a

12.01b 12.12b 11.23b

13.71%c 13.85%c 19.78%c

0.032c 0.032c 0.029c

6.25d,e 7.6d 7.4b,f

Source: data collected from (a) [44]; (b) [60]; (c) FASOM at Texas A&M University, (d) [63]; (e) [64]; and (f) [65].

3.4. Data Data have been taken from various published government statistics and research reports including the Taiwan Agricultural Yearbook,2 Trade Statistics of the Inspectorate-General of Customs,3 Taiwan Agricultural Prices and Costs Monthly,4 Taiwan Area Agricultural Products Wholesale Market Yearbook,5 Production Cost and Income of Farm Products Statistics,6 Forestry Statistics of Taiwan,7 and Commodity Price Statistics Monthly.8 Parameters such as biomass conversion rates, emission offset capability, unit processing costs, and price elasticities are obtained from literature, personal communications, and data calculation.

2 Datasets used from this source contains crop types, crop output, fertilizer use, seed use, and irrigation use. The data are specified to the sub-regional level (i.e., county level). 3 Tariffs, import quota of every commodity have been collected. 4 The monthly prices of agricultural commodities have been collected from this source. 5 Quantity demand and quantity supply of commodities have been collected to calculate elasticities. 6 Land rent, interest cost, and input costs have been collected from this source. 7 Land availability, distribution, and temporal changes have been collected. 8 Prices of secondary commodities such as livestock products and feedstuffs have been collected.

4.1. Discussion The results of ethanol production and GHG effects are shown in Table 3. In this table, we have 3 scenarios reflecting changes in crop yields (i.e., ignore yield change, yield projection A, and yield projection B) and 15 scenarios reflecting changes in gasoline and GHG prices so that the bioenergy production and emission offset consequences can be easily compared. Ceteris paribus, climate-induced crop yield change exerts a significant influence on Taiwanese bioenergy development while higher gasoline and GHG prices encourage ethanol production. When climate-induced yield change and crop by-products are not taken into account, we show that to meet the target demand (300 million liters), the gasoline price must be higher than NT$30 per liter; otherwise only 73e92% of ethanol demand can be satisfied. The results indicate that the byproducts of sweet potato help in increasing ethanol production. When the by-products are utilized, net ethanol production is increased by 17.92%e18.43%, depending on the level of gasoline prices. Therefore, when both sweet potato and its by-products are simultaneously utilized, a lower gasoline price (NT$25/liter) is sufficient to meet the target, and the deficiency reduces from 27% to approximately 11% when the gasoline price stays at an even lower level (i.e. NT$20). In most cases, the demand can be satisfied when by-products is jointly used, providing an additional margin of safety in cases of low gasoline prices. Climate-induced yield change also affects bioenergy production. When crop yields are uncertain, farmers with marginal land are more likely to participate in energy crop programs to gain certain benefits (i.e., subsidy as well as crop sales). Under such circumstances, demand for ethanol is generally fulfilled and in some cases a surplus of 111 million liters of ethanol is observed. The results show that if crop yields fall due to climate change, ethanol production too, will usually fall because a decrease in production of other crops drives up their prices, and consequently more land will be dedicated to planting such crops and less feedstock will be available for ethanol production. In either case (with or without climate-induced yield change), we find that the utilization of the by sweet potato vines, as well as the higher GHG prices, can stabilize net ethanol production. The availability of by-products of sweet potato merits more discussion. By-products are produced in accompaniment to crops, and thus, if renewable energy policies or programs do not provide enough incentives to participants, a dramatic shrink in ethanol production could occur (i.e., due to less of feedstocks). Although this situation may be alleviated by enhancing the government subsidy in energy crop programs, promotion policies incur costs to the whole society. Unless other benefits such as mitigation of climate change and an increase in energy security can offset this cost, application of such high subsidy must be done selectively and carefully, keeping social welfare in mind. The results show that only sweet potatoes will be planted in energy crop programs. This is because sweet potatoes have a relatively higher yield and a relatively lower processing cost than other alternatives, making sweet potato a very competitive choice in energy crop selection. As shown in Table 4, more than 52% of aside land is engaged in sweet potato plantation. The quantity of land used is increased when gasoline prices shoot up, and a 74% increase will occur when gasoline prices is at NT$30 per liter. An expansion in ethanol production also occurs at this gasoline price because an increase in sweet potato acreage implies an increase in availability of sweet potato vines (see Table 3). When GHG price increases, planted area increases by approximately 8%, and as long as the GHG price remains at a high level,

C.-C. Kung / Energy 167 (2019) 1051e1064

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Table 3 Bioenergy production and Emission reduction under climate impacts. GHG Price

NT$/ton

5

5

5

5

5

Gasoline Price

NT$/liter

20

25

30

35

40

1000 L 1000 L ton

218,553.9 49,371.3 30,007.6

274,636.2 60,808.4 37,569.8

323,401.5 70,718.5 44,141.4

324,978.1 70,986.3 44,348.0

324,313.2 70,845.5 44,257.8

1000 L 1000 L ton

337,267.1 73,488.1 46,004.6

337,276.3 73,490.1 46,005.8

337,279.7 73,490.8 46,006.3

336,793.1 73,388.6 45,940.4

337,637.4 73,566.6 46,054.8

1000 L 1000 L ton

337,220.5 73,478.5 45,998.3

337,229.7 73,480.5 45,999.5

337,072.8 73,447.4 45,978.3

336,746.5 73,379.0 45,934.1

337,590.8 73,556.7 46,048.5

GHG Price

NT$/ton

25

25

25

25

25

Gasoline Price

NT$/liter

20

25

30

35

40

1000 L 1000 L ton

232,289.7 52,173.4 31,859.9

276,095.9 60,985.3 37,753.1

323,401.5 70,718.5 44,141.4

332,092.8 72,422.0 45,305.7

324,893.5 70,967.3 44,336.4

1000 L 1000 L ton

337,267.5 73,488.2 46,004.6

337,282.4 73,491.4 46,006.7

337,120.5 73,457.2 45,984.7

338,965.2 73,845.3 46,234.8

337,637.4 73,566.6 46,054.8

1000 L 1000 L ton

337,220.9 73,478.6 45,998.3

337,230.4 73,480.6 45,999.6

337,073.9 73,447.6 45,978.4

339,081.0 73,869.7 46,250.5

337,591.1 73,556.8 46,048.6

Ignore Yield Change Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection A) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection B) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset

Ignore Yield Change Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection A) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection B) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset GHG Price

NT$/ton

50

50

50

50

50

Gasoline Price

NT$/liter

20

25

30

35

40

1000 L 1000 L ton

232,704.0 52,259.9 31,915.9

323,369.9 70,712.2 44,137.2

331,178.3 72,291.3 45,188.6

338,236.1 73,696.0 46,136.4

331,269.1 72,310.6 45,200.9

1000 L 1000 L ton

337,270.8 73,488.9 46,005.1

337,279.7 73,490.8 46,006.3

336,795.6 73,389.1 45,940.7

339,056.0 73,864.5 46,247.1

337,638.2 73,566.8 46,055.0

1000 L 1000 L ton

337,188.8 73,471.6 45,994.0

337,237.0 73,481.8 46,000.5

336,749.0 73,379.5 45,934.4

339,171.5 73,888.9 46,262.8

337,591.6 73,556.9 46,048.6

Ignore Yield Change Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection A) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset With Yield Change (Projection B) Ethanol Production (SWP) Ethanol Production (By-product) Emission Offset

gasoline prices do not have a considerable effect on energy crop plantation. This can be explained by the fact that the quality of marginal land cannot make profits at any simulated price because production costs in such land are too high to be competitive. We also show that, as long as the possibility of change in crop yield is low, a small decrease in crop yield will result in a 0.2% decrease in planted area. In general, economic factors such as gasoline and GHG prices and promotion programs, like offering subsidies, play an important role in Taiwan's bioenergy production. A decrease in crop yield change impacts bioenergy production, but its impact diminishes at higher GHG and gasoline prices, and can be partially recovered by the use of by-products of sweet potatoes. However, this is not to say that the change in crop yield change has no impact. As will be seen, it does have considerable impacts on cropland utilization, resource allocation, and allocation of government subsidies. Government subsidy is an important factor in encouraging farmers' participation. The results show that the government expense may increase from NT$3.2 to 5.57 billion dollars or by 74.46% when gasoline price increases. A comparison of Tables 3 and 4 leads to a finding that merits further discussion. At low gasoline prices, an increase of 56.08 million liters of ethanol will cost an

additional NT$1102 million dollars, implying that a subsidy of NT$19.65 is required for every additional liter of ethanol. The subsidy increases by 34.32% on a per liter basis, which is a reflection of the fact that a portion of less fertile land has been engaged for energy crop plantation. However, if we take the by-products into account, per liter subsidy on ethanol can be lowered by 18.13%. This is because crop by-products can be used to produce ethanol, but no additional subsidy is required for growth of by-products, and thus the per liter subsidy on net ethanol production can be lowered. Table 2 illustrates another interesting finding: Climate-induced fluctuation in crop yield will result in a considerable change in land-use that lowers the per liter subsidy. That is, in the face of a change in crop yield, plantation patterns of existing cultivars and land use are very likely to alter to achieve efficiency (see Appendix for details). Rice is the major agricultural commodity in Taiwan and approximately 30% of cropland is used for rice production. Fig. 1 shows the cultivation area of rice under climate impacts. When climate-induced crop yield change is less likely to happen (i.e., probability less than 35%), rice production will not be affected since a potential small reduction in rice yield still yields higher expected revenue than other crops. However, as climate impact becomes

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C.-C. Kung / Energy 167 (2019) 1051e1064

Table 4 Energy crop planted area and social economic measures. GHG Price

NT$/ton

5

5

5

5

5

Gasoline Price

NT$/liter

20

25

30

35

40

1000 ha Million NT$ Million NT$

64.0 3197.3 11,775.7

86.0 4299.6 14,903.1

109.0 5447.8 18,075.2

111.5 5576.6 27,837.0

111.4 5569.0 33,773.7

1000 ha Million NT$ Million NT$

117.9 5894.4 17,348.4

117.9 5894.5 21,685.6

117.9 5894.6 26,022.8

117.8 5887.4 34,785.0

118.0 5898.6 39,771.7

1000 ha Million NT$ Million NT$

117.9 5893.6 17,347.7

117.9 5893.7 21,684.8

117.8 5891.7 26,018.3

117.7 5886.6 34,784.2

118.0 5897.8 39,770.7

GHG Price

NT$/ton

25

25

25

25

25

Gasoline Price

NT$/liter

20

25

30

35

40

1000 ha Million NT$ Million NT$

69.1 3453.2 11,811.6

89.6 4482.2 14,907.9

109.0 5447.8 18,075.2

115.6 5781.8 29,577.2

111.6 5577.6 33,775.8

1000 ha Million NT$ Million NT$

117.89 5894.41 17348.36

117.89 5894.65 21685.72

117.85 5892.52 26019.33

118.36 5917.99 34824.14

117.97 5898.61 39771.67

1000 ha Million NT$ Million NT$

117.9 5893.6 17,347.7

117.9 5893.7 21,684.8

117.8 5891.7 26,018.3

118.4 5919.5 34,826.2

118.0 5897.8 39,770.7

Ignore Yield Change Planted Area Government Expense Social Welfare Yield Change Projection A Planted Area Government Expense Social Welfare Yield Change Projection B Planted Area Government Expense Social Welfare

Ignore Yield Change Planted Area Government Expense Social Welfare Yield Change Projection A Planted Area Government Expense Social Welfare Yield Change Projection B Planted Area Government Expense Social Welfare GHG Price

NT$/ton

50

50

50

50

50

Gasoline Price

NT$/liter

20

25

30

35

40

1000 ha Million NT$ Million NT$

69.2 3458.9 11,812.7

108.9 5446.9 15,062.6

113.2 5660.5 18,105.7

118.0 5900.3 29,597.2

113.2 5661.7 33,799.5

1000 ha Million NT$ Million NT$

117.9 5894.5 17,348.4

117.9 5894.6 21,685.7

117.8 5887.4 26,012.3

118.4 5919.2 34,825.8

118.0 5898.6 39,771.7

1000 ha Million NT$ Million NT$

117.9 5893.4 17,347.2

117.9 5893.8 21,684.9

117.7 5886.6 26,011.2

118.4 5920.7 34,827.9

118.0 5897.8 39,770.7

Ignore Yield Change Planted Area Government Expense Social Welfare Yield Change Projection A Planted Area Government Expense Social Welfare Yield Change Projection B Planted Area Government Expense Social Welfare

more likely, the cultivation pattern may alter considerably. With a larger reduction in rice yield, as shown in scenario B, more land will be used to plant rice to meet demand. Under this circumstance, expected profits from rice plantation will be higher and more land switches to rice production. When changes in crop yield become more certain, rice production will increase by up to 2.68%. Environmental benefit such as mitigation of climate change is of major interest in bioenergy development. Fig. 2 shows that bioenergy production can offset 46,200 tons of CO2 under climate impacts. However, at lower level of GHG prices, only 68.96% of the emission reduction can be achieved. The offset contribution made by the sweet potato vine is notable. On an average 18.07% of emission reduction comes from the use of sweet potato vines, implying that a proper use of by-products might have considerable positive influences both on production of biofuel as well as mitigation of climate change. The figure also indicates that the GHG price has a larger effect on CO2 reduction than does ethanol price. This result implies that when the impact of climate is uncertain, farmers are very likely to switch their cultivars for energy crop to earn definite profits and subsidies as per energy crop program. This information is also displayed in Appendix. The results provide clues about the impact of climate-induced

crop yield change on the agricultural sector. It is clear that the net bioenergy production responds more to price changes than to yield changes, implying that the small changes in crop yield may indeed alter the cultivation patterns of many other crops, but alternation of energy crop plantation is primarily influenced by energy prices. This finding can, thus, be very helpful in policy analysis by encouraging more focus on the economic factors such as energy prices and carbon trade mechanism, and possibly ignoring land-use effects resulting from climate change, as illustrated in Fig. 3. Fig. 3a shows the production costs under various environmental risks. When farmers perceive uncertainty associated with crop outputs, they will choose to maximize the expected revenues in accordance with the risk level, and thus fluctuations in productions occur. Fig. 3b and c depicts further details. The results show that the use of seeds and fertilizer will fluctuate when crop yields are highly uncertain, but the yield change per se does not vary a lot, which can be found in lines such as SeedA, SeedB, FertilizerA and FertilizerB. However, it is noteworthy that irrigation expenses do not agree under various crop yield changes, implying that even when the similar amounts of seeds and fertilizer are applied in the face of same risk level, land-use patterns will be changed and the irrigation requirements for different farmland will

C.-C. Kung / Energy 167 (2019) 1051e1064

1057

Fig. 1. Rice plantation under different crop yield responses.

Fig. 2. Emission offset under low and high gasoline & GHG prices.

be different. The results explain why certain shifts in land use do not have significant impacts on bioenergy production: when farmers perceive yield changes, they can adjust production in different regions to maintain the net supply of major commodities. Therefore, climate-induced change in crop yield may result in different resource allocation patterns, but the stability in the supply of bioenergy feedstocks supply is assured. 4.2. Sensitivity analysis of yield shifting Climate-induced regional shifting of crop yield merits further discussion. For example, the northern area may become warmer and more humid and thus it may be more suitable for a certain type of crop or change in crop yield among regions may vary; however, such a shift is usually more obvious on large land (i.e., the U.S. or China), even on a global scale. Although Taiwan is a small island and the shifting of crop yield may be trivial, we need to validate this assumption and verify whether the shift would alter our results. We adopt a sensitivity analysis to examine the different yield shifting patterns for northern and southern Taiwan (Table 5). We show that if the change in crop yields is 1% different between

northern and southern Taiwan, the change of results is generally less than 0.01%. Based on the sensitivity test, the assumption used in this study {i.e., the adoption of Chang et al.‘s [35] estimates and the assumption that yield changes across all cropland are the same} and the simulated results may be valid. 4.3. Policy implications The study presents several important results in the face of impacts of climate change and different market conditions. We show that, ceteris paribus, energy and GHG prices have larger influences on bioenergy production and emission offset than do crop yield responses. While biofuel production is relatively stable under climate-induced crop yield change, there are considerable influences on rice cultivation, government expenditures on supporting programs, agricultural resource allocation, and land-use patterns. Some policy implications are also discussed: (1) Policies should be designed to consider indirect environmental values of bioenergy production. The value of direct emission reduction from ethanol production is incorporated into the welfare analysis, but there might be some indirect

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C.-C. Kung / Energy 167 (2019) 1051e1064

Fig. 3. The influences of climate-induced crop yield change in agricultural sector.

values that have not been taken into account. For example, when residents realize that the use of clean energy (i.e., renewable energy) can help sustain our environment, they may tend to consume less fossil fuel and use more clean energy, or simply reduce wasteful behavior, all of which can potentially reduce GHG emissions whose values have not been included. The conditional valuation methods (CVM)

may be used to assess these indirect benefits. If we don't take indirect environmental values into account, the net value of emission reduction from bioenergy production may be understated. Therefore, when designing bioenergy promotion policies, appraising such aggregated environmental benefits appropriately may be necessary.

C.-C. Kung / Energy 167 (2019) 1051e1064

5. Conclusions

Table 5 Sensitivity analysis of regional shifting of crops yields.

Ethanol Production Energy Crop Area Emission Offset Energy Crop Subsidy Social Welfare

1059

Case A

Case B

Case C

Case D

0.0063% 0.0085% 0.0063% 0.0049% 0.0018%

0.0040% 0.0000% 0.0040% 0.0031% 0.0012%

0.0063% 0.0085% 0.0063% 0.0049% 0.0018%

0.0073% 0.0085% 0.0073% 0.0056% 0.0021%

Note: Case A: Yield Changes in Northern Taiwan (7 counties) are 1% larger than estimates of Chang et al. [35]. Case B: Yield Changes in Northern Taiwan (7 counties) are 1% smaller than estimates of Chang et al. [35]. Case C: Yield Changes in Southern Taiwan (8 counties) are 1% larger than estimates of Chang et al. [35]. Case D: Yield Changes in Southern Taiwan (8 counties) are 1% smaller than estimates of Chang et al. [35].

(2) Agricultural activities may be subject to considerable transformations due to climate change. As indicated by the results, biofuel production is affected slightly by the climate change, but agricultural resources seem to be allocated differently due to the shift of production activities across regions. As shown in Appendix, the cultivation patterns in moderate climate change scenarios have already altered considerably, and are expected to have a greater shift in the face of stronger climatic impacts. Therefore, to maintain a stable agricultural system and commodity market, agricultural policies must be adjusted to ensure the revenues obtained by farmers are not influenced too much; otherwise the cultivation patterns could be considerably different from the past and the overall impact on agricultural markets could be large. Although climate change does not always cause harm, depending on the crop characteristics and regional change in temperature and precipitation, it is prudent to consider the worst cases and prevent their occurrences. (3) Flexible support programs may be more effective in the face of uncertain climate impacts. We show that a small change in crop yield may induce considerable changes in resource allocation and choice of cultivars. Because farmers usually have less knowledge than the government officers about potential climate change and its influences (i.e., policy makers and experts), their production activities and land utilization may not be optimal if historical yield patterns are used to make planting decisions. Therefore, to ensure efficient land utilization under climate change (if any), flexible support programs should be designed so that farmers can easily adjust their production activity. Government officers thus need to make more efforts in predicting the potential influences of climate change, and probably redesign or reform existing policies to encourage farmers' participation.

The results show that in most cases Taiwan's ethanol production can meet its E3-gasoline requirement, implying that if Taiwan is able to produce more fuels domestically, the impacts from international market distortions (i.e., political issues and price volatility) can be lessened, and Taiwan's energy security can be improved. The study illustrates several interesting findings. First, the results show that a small change in crop yield will not affect ethanol production considerably, but potential alterations in resource allocation and land-use patterns do occur. Second, ethanol is primarily influenced by gasoline and GHG prices; the latter is considered to have a larger impact. The smoothness of ethanol production is due to the employment of support programs such as set-aside land program and energy crop subsidy program. Third, although the emission reduction from ethanol production and utilization cannot contribute much on a global scale, additional benefits that may be obtained from Taiwan's ethanol production are that people may possibly change their energy consumption pattern (from a wasteful to a more conservative manner), which would further improve Taiwan's energy security and reduce GHG emissions. It is noteworthy to address some limitations embedded in this study. The study only examines the moderate climate change scenarios and concludes that the bioenergy production can be stabilized as long as the energy and GHG prices do not change greatly. However, a stronger climate-induced impact may actually occurs, resulting in a greater change in agricultural activities and consequently, altering biofuel production. In addition, the sweet potato vine is the only crop by-product considered in this study. Since rice production is the most popular crop in Taiwan, rice straw can be a good candidate. Further researches may diminish these assumptions and accommodate other alternatives to provide more robust studies. Acknowledgements The author thanks the financial support from the National Natural Science Foundation (71663022; 41861042; 71864013), The Distinguished Young Scholar Program of Jiangxi Province (20171BCB23047), University Liberal Art and Social Science Foundation of Jiangxi (JC17205), and Scientific Program of Jiangxi’s Department of Education (GJJ160437). Thanks for the assistance of Dr. Bruce McCarl at Texas A&M University and Dr. Chi-Chung Chen at National Chung-Hsing University for their constructive opinions. I also thank Dr. Wei Huang, the assistant professor in National University of Singapore, for personal discussions. Appendix

Table A Simulated planted hectare of various under different climate change scenarios. Scenario A Probability of crop yield change

JAPONICA SOYBEAN SWPOTATO TEA SESAME GINGER

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

186.68 0.47 5.06 9.64 0.48 0.89

186.76 0.47 5.12 9.64 0.48 0.89

187.22 0.47 5.18 9.64 0.48 0.89

187.22 0.47 5.18 9.64 0.48 0.89

187.16 0.47 5.18 9.71 0.48 0.9

184.88 0.48 5.1 9.76 0.48 0.85

184.63 0.47 5.18 9.71 0.48 0.9

186.28 0.47 5.08 9.71 0.48 0.9

186.18 0.29 5.25 9.96 0.31 0.85

186.17 0.31 5.33 9.91 0.32 0.84

(continued on next page)

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C.-C. Kung / Energy 167 (2019) 1051e1064

Table A (continued ) GARBULB ASPARA CAULI CUCUM PEA CANTA PONKAN LIUCHENG LEMON BETEL GRAPE PEACH LICHE APPLE PASSION GLADIO OTHERFLO CORN PEANUT SWPOTATO1 CANEPROC RADISH SCALLION LEEK WATERBA CHINESECAB BITTER VESOY BANANA TANKAN LONGAN GRAPEFUR GUAVA LOQUAT PERSIM CARAM PAPAYA COCONUT ROSE SORGHUM ADZUKI POTATO CANEFRESH CARROT ONION BAMBOO CABBAGE MUSTARD TOMATO WAMELON PINEAPPLE WENTAN JUJUBE MANGO WAXAPPLE PLUM APRICOT PEAR SUGARAP CHRYSAN BABYS

4.61 1.5 2.8 1.47 1.33 1.74 4.95 6.9 0.28 25.09 1.9 1.14 8.58 0.54 0.19 0.37 0.76 23.74 30.89 69.79 36.46 1.04 3.86 0.74 1.25 2.93 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.09 0.16 15.29 1.15 0.88 1.64 0.22 0.16 17.99 3.54 1.6 3.47 10.14 3.31 2.91 0.75 9.36 0.75 2.5 6.75 4.01 2.52 1.26 0.03

4.61 1.5 2.8 1.47 1.33 1.7 4.94 6.9 0.28 25.09 1.9 1.14 8.57 0.54 0.19 0.37 0.76 23.75 30.71 69.18 36.46 1.07 3.86 0.74 1.25 2.93 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.2 0.16 17.99 3.53 1.63 3.5 10.2 3.31 2.91 0.75 9.36 0.75 2.5 6.75 4 2.52 1.26 0.03

4.61 1.5 2.8 1.47 1.33 1.43 4.94 6.9 0.28 25.09 1.9 1.14 8.57 0.53 0.19 0.37 0.76 11.58 28.46 85.89 36.46 0.6 3.86 0.74 1.25 2.78 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.45 0.16 17.99 3.55 1.49 3.48 9.87 3.31 2.91 0.75 9.36 0.75 2.5 6.74 3.99 2.52 1.26 0.03

4.61 1.5 2.8 1.47 1.33 1.42 4.94 6.9 0.28 25.09 1.9 1.14 8.57 0.53 0.19 0.37 0.76 11.58 28.46 85.91 36.46 0.6 3.86 0.74 1.25 2.78 0.98 3.95 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.45 0.16 17.99 3.54 1.49 3.48 9.86 3.32 2.91 0.75 9.36 0.75 2.5 6.74 3.99 2.52 1.26 0.03

4.61 1.5 2.8 1.47 1.32 1.44 4.92 6.82 0.28 25.37 1.9 1.14 8.55 0.53 0.19 0.37 0.76 11.58 28.33 101.8 36.46 0.54 3.86 0.74 1.24 2.67 0.98 3.95 6.25 1.16 9.33 0.74 4.62 0.5 1.11 1.15 1.69 1.1 0.16 15.29 1.15 0.88 1.62 0.46 0.16 17.91 3.65 1.43 3.46 9.97 3.32 2.91 0.75 9.36 0.75 2.44 6.75 3.98 2.52 1.26 0.03

4.6 1.22 2.94 1.45 1.33 1.37 5.14 6.96 0.29 25.01 1.9 1.13 8.63 0.54 0.2 0.36 0.79 11.7 22.86 108.24 36.79 0.52 3.91 0.74 1.28 2.37 0.98 3.93 6.36 1.18 9.32 0.73 4.6 0.5 1.11 1.16 1.68 1.1 0.16 14.19 1.1 0.91 1.58 0.43 0.17 18.11 3.64 1.28 3.6 9.45 3.23 2.91 0.75 9.36 0.76 2.7 6.79 4.02 2.52 1.26 0.04

4.6 1.49 2.8 1.47 1.31 1.56 4.93 6.82 0.28 25.37 1.93 1.15 8.56 0.53 0.19 0.36 0.77 11.09 22.88 118.26 36.38 0.49 3.89 0.75 1.24 2.26 0.97 3.98 6.26 1.17 9.33 0.74 4.62 0.51 1.12 1.15 1.69 1.1 0.16 15.3 1.15 0.89 1.61 0.41 0.17 17.94 3.63 1.3 3.57 9.32 3.32 2.91 0.75 9.36 0.75 2.44 6.74 4.02 2.52 1.24 0.03

4.6 1.49 2.8 1.47 1.31 1.67 4.93 6.84 0.28 25.37 1.93 1.15 8.56 0.53 0.19 0.36 0.78 10.42 22.99 118.41 36.42 0.5 3.89 0.75 1.24 2.19 0.97 3.97 6.26 1.18 9.33 0.74 4.62 0.51 1.12 1.15 1.7 1.09 0.16 15.35 1.13 0.89 1.62 0.35 0.17 17.95 3.59 1.35 3.7 9.08 3.31 2.92 0.75 9.36 0.75 2.5 6.73 4.02 2.55 1.24 0.03

5.27 0.96 1.37 1.53 0.42 1.41 6.02 8.89 0.38 27.98 1.1 1.64 8.09 0.7 0.18 0.25 0.55 10.78 22.97 117.98 36.86 0.43 3.98 0.21 1.22 2.15 0.72 5.34 6.74 1.28 9.65 0.76 3.08 0.68 0.82 0.94 1.69 1.27 0.18 15.36 2.24 1.36 1.31 0.26 0.23 17.24 3.75 1.19 3.92 9.06 3.29 2.74 0.87 9.67 2.38 2.46 6.91 4.78 2.56 0.19 0.02

5.21 1.01 1.47 1.54 0.47 1.41 6.02 8.89 0.38 27.83 1.13 1.67 8.19 0.71 0.18 0.24 0.57 10.78 22.96 117.89 37.11 0.44 3.97 0.26 1.22 2.15 0.74 5.27 6.77 1.29 9.65 0.76 3.16 0.68 0.83 0.96 1.68 1.27 0.18 15.52 2.23 1.38 1.33 0.26 0.24 17.37 3.74 1.2 3.92 9.06 3.3 2.74 0.87 9.67 2.37 2.48 6.93 4.79 2.56 0.25 0.02

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

JAPONICA SOYBEAN SWPOTATO TEA SESAME GINGER GARBULB ASPARA CAULI CUCUM PEA CANTA PONKAN LIUCHENG

186.14 0.31 5.33 9.92 0.32 0.84 5.21 1 1.46 1.53 0.46 1.4 6.02 8.89

186.14 0.47 5.33 9.7 0.49 0.9 4.61 1.5 2.8 1.47 1.31 1.51 4.94 6.81

185.86 0.47 5.17 9.75 0.5 0.85 4.61 1.22 2.94 1.46 1.33 1.6 5.16 6.94

185.86 0.47 5.14 9.71 0.48 0.9 4.61 1.5 2.8 1.47 1.31 1.44 4.93 6.84

188.22 0.47 5.26 9.71 0.49 0.9 4.61 1.5 2.8 1.47 1.31 1.46 4.93 6.82

188.29 0.47 5.26 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.44 4.93 6.79

188.29 0.47 5.26 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.44 4.93 6.79

187.83 0.47 5.2 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.74 4.93 6.8

187.75 0.47 5.14 9.7 0.49 0.9 4.62 1.5 2.8 1.47 1.31 1.75 4.94 6.8

187.78 0.48 4.79 9.74 0.48 0.79 4.59 1.23 3 1.32 1.29 1.99 5.47 6.86

C.-C. Kung / Energy 167 (2019) 1051e1064

1061

Table A (continued ) LEMON BETEL GRAPE PEACH LICHE APPLE PASSION GLADIO OTHERFLO CORN PEANUT SWPOTATO1 CANEPROC RADISH SCALLION LEEK WATERBA CHINESECAB BITTER VESOY BANANA TANKAN LONGAN GRAPEFUR GUAVA LOQUAT PERSIM CARAM PAPAYA COCONUT ROSE SORGHUM ADZUKI POTATO CANEFRESH CARROT ONION BAMBOO CABBAGE MUSTARD TOMATO WAMELON PINEAPPLE WENTAN JUJUBE MANGO WAXAPPLE PLUM APRICOT PEAR SUGARAP CHRYSAN BABYS Scenario B

0.38 27.85 1.13 1.66 8.17 0.71 0.18 0.25 0.57 10.78 22.9 117.87 37.08 0.43 3.97 0.25 1.22 2.14 0.74 5.28 6.77 1.29 9.65 0.76 3.15 0.68 0.82 0.96 1.68 1.27 0.18 15.5 2.23 1.37 1.33 0.25 0.24 17.35 3.77 1.19 3.92 9.05 3.3 2.74 0.87 9.67 2.38 2.48 6.92 4.79 2.56 0.24 0.02

0.28 25.36 1.94 1.15 8.59 0.54 0.19 0.36 0.77 10.32 22.96 118.32 36.26 0.43 3.89 0.75 1.24 2.19 0.97 4.01 6.27 1.19 9.41 0.74 4.64 0.51 1.12 1.16 1.72 1.08 0.16 15.08 1.22 0.88 1.62 0.36 0.17 18 3.59 1.2 3.66 9.19 3.35 2.93 0.75 9.45 0.76 2.53 6.72 4.04 2.54 1.24 0.03

0.29 25 1.91 1.15 8.67 0.55 0.2 0.36 0.79 11.41 22.89 108.65 36.64 0.5 3.91 0.74 1.29 2.25 0.98 3.97 6.38 1.19 9.41 0.74 4.61 0.51 1.12 1.17 1.69 1.11 0.16 13.91 1.2 0.91 1.58 0.42 0.17 18.17 3.81 1.36 3.61 9.6 3.27 2.92 0.75 9.44 0.76 2.72 6.81 4.08 2.55 1.26 0.05

0.28 25.36 1.93 1.14 8.57 0.53 0.19 0.36 0.77 11.7 22.89 108.41 36.29 0.5 3.89 0.75 1.24 2.34 0.97 4 6.26 1.21 9.42 0.74 4.63 0.5 1.11 1.16 1.74 1.07 0.16 15.14 1.2 0.88 1.62 0.45 0.17 18.02 3.89 1.3 3.6 9.67 3.33 2.93 0.75 9.45 0.76 2.6 6.71 4.01 2.54 1.24 0.03

0.28 25.36 1.93 1.14 8.58 0.53 0.19 0.36 0.77 11.59 28.4 101.64 36.27 0.54 3.89 0.75 1.24 2.68 0.97 4 6.27 1.2 9.41 0.74 4.63 0.5 1.11 1.16 1.72 1.08 0.16 15.1 1.21 0.88 1.61 0.47 0.17 18.01 3.68 1.44 3.49 10.03 3.34 2.93 0.75 9.45 0.76 2.56 6.72 4.01 2.54 1.24 0.03

0.28 25.36 1.94 1.15 8.59 0.53 0.19 0.36 0.77 11.59 28.52 85.76 36.22 0.6 3.89 0.75 1.24 2.79 0.97 4.02 6.27 1.17 9.41 0.74 4.64 0.51 1.12 1.16 1.69 1.11 0.16 15.01 1.24 0.88 1.61 0.46 0.17 17.99 3.58 1.5 3.51 9.93 3.36 2.92 0.75 9.45 0.76 2.45 6.75 4.03 2.54 1.24 0.03

0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 11.59 28.53 85.73 36.22 0.6 3.89 0.75 1.24 2.79 0.97 4.01 6.28 1.17 9.41 0.74 4.64 0.51 1.12 1.16 1.69 1.11 0.16 15.01 1.24 0.88 1.61 0.46 0.17 17.99 3.58 1.5 3.51 9.93 3.36 2.92 0.75 9.45 0.76 2.45 6.75 4.04 2.54 1.24 0.03

0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 23.76 30.78 68.97 36.22 1.07 3.89 0.75 1.24 2.94 0.97 4.01 6.28 1.17 9.42 0.74 4.64 0.51 1.12 1.16 1.7 1.1 0.16 15.02 1.24 0.88 1.61 0.22 0.17 17.99 3.56 1.63 3.56 10.27 3.35 2.92 0.75 9.45 0.76 2.47 6.75 4.04 2.54 1.24 0.03

0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 23.75 30.95 69.63 36.22 1.04 3.89 0.75 1.24 2.94 0.97 4.01 6.27 1.17 9.42 0.74 4.64 0.51 1.12 1.16 1.7 1.1 0.16 15.02 1.24 0.88 1.61 0.23 0.17 17.99 3.57 1.61 3.5 10.21 3.35 2.92 0.75 9.45 0.76 2.47 6.75 4.04 2.54 1.24 0.03

0.3 24.34 1.97 1.31 8.75 0.64 0.2 0.34 0.8 22.74 31.03 68.11 32.68 0.99 3.88 0.71 1.39 2.81 0.93 3.94 6.34 1.11 9.41 0.62 4.57 0.56 0.83 1.15 1.57 0.94 0.16 12.28 1.13 1.21 1.61 0.4 0.17 18.59 3.39 1.61 3.53 10.18 3.37 2.09 0.75 9.45 0.75 2.52 6.53 4.11 2.57 1.25 0.04

Probability of crop yield change

JAPONICA SOYBEAN SWPOTATO TEA SESAME GINGER GARBULB ASPARA CAULI CUCUM PEA CANTA PONKAN LIUCHENG LEMON BETEL GRAPE PEACH

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

186.68 0.47 5.06 9.64 0.48 0.89 4.61 1.5 2.8 1.47 1.33 1.74 4.95 6.9 0.28 25.09 1.9 1.14

186.76 0.47 5.12 9.64 0.48 0.89 4.61 1.5 2.8 1.47 1.33 1.7 4.94 6.9 0.28 25.09 1.9 1.14

187.22 0.47 5.18 9.64 0.48 0.89 4.61 1.5 2.8 1.47 1.33 1.43 4.94 6.9 0.28 25.09 1.9 1.14

187.22 0.47 5.18 9.64 0.48 0.89 4.61 1.5 2.8 1.47 1.33 1.42 4.94 6.9 0.28 25.09 1.9 1.14

187.16 0.47 5.18 9.71 0.48 0.9 4.61 1.5 2.8 1.47 1.32 1.44 4.92 6.82 0.28 25.37 1.9 1.14

184.88 0.48 5.1 9.76 0.48 0.85 4.6 1.22 2.94 1.45 1.33 1.37 5.14 6.96 0.29 25.01 1.9 1.13

184.63 0.47 5.18 9.71 0.48 0.9 4.6 1.49 2.8 1.47 1.31 1.56 4.93 6.82 0.28 25.37 1.93 1.15

187.54 0.47 5.19 9.71 0.48 0.9 4.6 1.49 2.8 1.47 1.31 1.67 4.93 6.84 0.28 25.37 1.93 1.15

187.52 0.29 5.24 9.96 0.31 0.85 5.27 0.96 1.37 1.53 0.42 1.4 6.02 8.89 0.38 27.98 1.1 1.64

187.51 0.31 5.37 9.91 0.32 0.84 5.21 1.01 1.47 1.54 0.47 1.4 6.02 8.89 0.38 27.83 1.13 1.67

(continued on next page)

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C.-C. Kung / Energy 167 (2019) 1051e1064

Table A (continued ) LICHE APPLE PASSION GLADIO OTHERFLO CORN PEANUT SWPOTATO1 CANEPROC RADISH SCALLION LEEK WATERBA CHINESECAB BITTER VESOY BANANA TANKAN LONGAN GRAPEFUR GUAVA LOQUAT PERSIM CARAM PAPAYA COCONUT ROSE SORGHUM ADZUKI POTATO CANEFRESH CARROT ONION BAMBOO CABBAGE MUSTARD TOMATO WAMELON PINEAPPLE WENTAN JUJUBE MANGO WAXAPPLE PLUM APRICOT PEAR SUGARAP CHRYSAN BABYS

8.58 0.54 0.19 0.37 0.76 23.74 30.89 69.79 36.46 1.04 3.86 0.74 1.25 2.93 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.09 0.16 15.29 1.15 0.88 1.64 0.22 0.16 17.99 3.54 1.6 3.47 10.14 3.31 2.91 0.75 9.36 0.75 2.5 6.75 4.01 2.52 1.26 0.03

8.57 0.54 0.19 0.37 0.76 23.75 30.71 69.18 36.46 1.07 3.86 0.74 1.25 2.93 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.2 0.16 17.99 3.53 1.63 3.5 10.2 3.31 2.91 0.75 9.36 0.75 2.5 6.75 4 2.52 1.26 0.03

8.57 0.53 0.19 0.37 0.76 11.58 28.46 85.89 36.46 0.6 3.86 0.74 1.25 2.78 0.98 3.94 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.45 0.16 17.99 3.55 1.49 3.48 9.87 3.31 2.91 0.75 9.36 0.75 2.5 6.74 3.99 2.52 1.26 0.03

8.57 0.53 0.19 0.37 0.76 11.58 28.46 85.91 36.46 0.6 3.86 0.74 1.25 2.78 0.98 3.95 6.26 1.17 9.33 0.74 4.61 0.5 1.11 1.16 1.68 1.1 0.16 15.29 1.15 0.88 1.63 0.45 0.16 17.99 3.54 1.49 3.48 9.86 3.32 2.91 0.75 9.36 0.75 2.5 6.74 3.99 2.52 1.26 0.03

8.55 0.53 0.19 0.37 0.76 11.58 28.33 101.8 36.46 0.54 3.86 0.74 1.24 2.67 0.98 3.95 6.25 1.16 9.33 0.74 4.62 0.5 1.11 1.15 1.69 1.1 0.16 15.29 1.15 0.88 1.62 0.46 0.16 17.91 3.65 1.43 3.46 9.97 3.32 2.91 0.75 9.36 0.75 2.44 6.75 3.98 2.52 1.26 0.03

8.63 0.54 0.2 0.36 0.79 11.7 22.86 108.24 36.79 0.52 3.91 0.74 1.28 2.37 0.98 3.93 6.36 1.18 9.32 0.73 4.6 0.5 1.11 1.16 1.68 1.1 0.16 14.19 1.1 0.91 1.58 0.43 0.17 18.11 3.64 1.28 3.6 9.45 3.23 2.91 0.75 9.36 0.76 2.7 6.79 4.02 2.52 1.26 0.04

8.56 0.53 0.19 0.36 0.77 11.09 22.88 118.26 36.38 0.49 3.89 0.75 1.24 2.26 0.97 3.98 6.26 1.17 9.33 0.74 4.62 0.51 1.12 1.15 1.69 1.1 0.16 15.3 1.15 0.89 1.61 0.41 0.17 17.94 3.63 1.3 3.57 9.32 3.32 2.91 0.75 9.36 0.75 2.44 6.74 4.02 2.52 1.24 0.03

8.56 0.53 0.19 0.36 0.78 10.12 22.93 118.59 36.42 0.49 3.89 0.75 1.24 2.18 0.97 3.97 6.26 1.18 9.33 0.74 4.62 0.51 1.12 1.15 1.7 1.09 0.16 15.35 1.13 0.89 1.62 0.35 0.17 17.95 3.61 1.35 3.69 9.14 3.31 2.92 0.75 9.36 0.75 2.5 6.73 4.02 2.55 1.24 0.03

8.09 0.7 0.18 0.25 0.55 10.79 22.94 118.02 36.86 0.43 3.98 0.21 1.22 2.15 0.72 5.34 6.74 1.28 9.65 0.76 3.08 0.68 0.82 0.94 1.69 1.27 0.18 15.36 2.24 1.36 1.31 0.25 0.23 17.24 3.78 1.19 3.92 9.12 3.29 2.74 0.87 9.67 2.38 2.46 6.91 4.78 2.56 0.19 0.02

8.19 0.71 0.18 0.24 0.57 10.79 22.93 117.87 37.11 0.44 3.97 0.26 1.22 2.15 0.74 5.27 6.77 1.29 9.65 0.76 3.16 0.68 0.83 0.96 1.68 1.27 0.18 15.52 2.23 1.38 1.33 0.25 0.24 17.37 3.77 1.2 3.92 9.12 3.3 2.74 0.87 9.67 2.37 2.48 6.93 4.79 2.56 0.25 0.02

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

JAPONICA SOYBEAN SWPOTATO TEA SESAME GINGER GARBULB ASPARA CAULI CUCUM PEA CANTA PONKAN LIUCHENG LEMON BETEL GRAPE PEACH LICHE APPLE PASSION GLADIO OTHERFLO CORN PEANUT SWPOTATO1

187.47 0.31 5.37 9.92 0.32 0.84 5.21 1 1.46 1.53 0.46 1.4 6.02 8.89 0.38 27.85 1.13 1.66 8.17 0.71 0.18 0.25 0.57 10.79 22.86 117.85

187.55 0.47 5.3 9.7 0.49 0.9 4.61 1.5 2.8 1.47 1.31 1.43 4.94 6.81 0.28 25.36 1.94 1.15 8.59 0.54 0.19 0.36 0.77 10.41 22.93 118.32

187.6 0.47 5.21 9.75 0.5 0.85 4.61 1.22 2.94 1.46 1.33 1.59 5.16 6.94 0.29 25 1.91 1.15 8.67 0.55 0.2 0.36 0.79 10.43 22.95 109.38

187.64 0.47 5.17 9.71 0.48 0.9 4.61 1.5 2.8 1.47 1.31 1.44 4.93 6.84 0.28 25.36 1.93 1.14 8.57 0.53 0.19 0.36 0.77 10.72 22.98 109.14

189.62 0.47 5.3 9.71 0.49 0.9 4.61 1.5 2.8 1.47 1.31 1.45 4.93 6.82 0.28 25.36 1.93 1.14 8.58 0.53 0.19 0.36 0.77 11.6 28.48 101.67

189.68 0.47 5.3 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.43 4.93 6.79 0.28 25.36 1.94 1.15 8.59 0.53 0.19 0.36 0.77 11.6 28.6 85.78

189.68 0.47 5.3 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.44 4.93 6.79 0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 11.6 28.6 85.75

189.68 0.47 5.26 9.7 0.49 0.89 4.62 1.5 2.8 1.47 1.31 1.69 4.93 6.8 0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 23.55 28.44 68.88

189.61 0.47 5.2 9.7 0.49 0.9 4.62 1.5 2.8 1.47 1.31 1.7 4.94 6.8 0.28 25.36 1.94 1.16 8.59 0.54 0.19 0.36 0.77 23.53 28.61 69.56

191.6 0.48 4.94 9.74 0.48 0.79 4.59 1.23 3 1.32 1.29 1.97 5.47 6.86 0.3 24.34 1.97 1.31 8.75 0.64 0.2 0.34 0.8 23.31 30.94 68.14

C.-C. Kung / Energy 167 (2019) 1051e1064

1063

Table A (continued ) CANEPROC RADISH SCALLION LEEK WATERBA CHINESECAB BITTER VESOY BANANA TANKAN LONGAN GRAPEFUR GUAVA LOQUAT PERSIM CARAM PAPAYA COCONUT ROSE SORGHUM ADZUKI POTATO CANEFRESH CARROT ONION BAMBOO CABBAGE MUSTARD TOMATO WAMELON PINEAPPLE WENTAN JUJUBE MANGO WAXAPPLE PLUM APRICOT PEAR SUGARAP CHRYSAN BABYS

37.08 0.44 3.97 0.25 1.22 2.14 0.74 5.28 6.77 1.29 9.65 0.76 3.15 0.68 0.82 0.96 1.68 1.27 0.18 15.5 2.23 1.37 1.33 0.25 0.24 17.35 3.81 1.2 3.93 9.11 3.3 2.74 0.87 9.67 2.38 2.48 6.92 4.79 2.56 0.24 0.02

36.26 0.49 3.89 0.75 1.24 2.27 0.97 4.01 6.27 1.19 9.41 0.74 4.64 0.51 1.12 1.16 1.72 1.08 0.16 15.08 1.22 0.88 1.62 0.36 0.17 18 3.67 1.29 3.67 9.23 3.35 2.93 0.75 9.45 0.76 2.53 6.72 4.04 2.54 1.24 0.03

36.64 0.5 3.91 0.74 1.29 2.18 0.98 3.97 6.38 1.19 9.41 0.74 4.61 0.51 1.12 1.17 1.69 1.11 0.16 13.91 1.2 0.91 1.58 0.33 0.17 18.17 3.64 1.36 3.68 9.18 3.27 2.92 0.75 9.44 0.76 2.72 6.81 4.08 2.55 1.26 0.05

36.29 0.49 3.89 0.75 1.24 2.26 0.97 4 6.26 1.21 9.42 0.74 4.63 0.5 1.11 1.16 1.74 1.07 0.16 15.14 1.2 0.88 1.62 0.36 0.17 18.02 3.98 1.32 3.67 9.33 3.33 2.93 0.75 9.45 0.76 2.6 6.71 4.01 2.54 1.24 0.03

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36.22 1.09 3.89 0.75 1.24 2.91 0.97 4.01 6.28 1.17 9.42 0.74 4.64 0.51 1.12 1.16 1.7 1.1 0.16 15.02 1.24 0.88 1.61 0.24 0.17 17.99 3.53 1.34 3.55 9.72 3.35 2.92 0.75 9.45 0.76 2.47 6.75 4.04 2.54 1.24 0.03

36.22 1.06 3.89 0.75 1.24 2.91 0.97 4.01 6.27 1.17 9.42 0.74 4.64 0.51 1.12 1.16 1.7 1.1 0.16 15.02 1.24 0.88 1.61 0.24 0.17 17.99 3.54 1.31 3.5 9.65 3.35 2.92 0.75 9.45 0.76 2.47 6.75 4.04 2.54 1.24 0.03

32.68 0.99 3.88 0.71 1.39 2.84 0.93 3.94 6.34 1.11 9.41 0.62 4.57 0.56 0.83 1.15 1.57 0.94 0.16 12.28 1.13 1.21 1.61 0.39 0.17 18.59 3.36 1.66 3.57 9.94 3.37 2.09 0.75 9.45 0.75 2.52 6.53 4.11 2.57 1.25 0.04

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