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Efficiency and economic benefit of darkfermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE) Duu-Hwa Lee Institute of Applied Economics, National Taiwan Ocean University, Keelung, Taiwan
highlights A soft-link DEA-CGE model was developed, and its effectiveness confirmed. Efficiency and improvement potential of dark fermentation technologies are provided. Efficiency of continuous technology significantly exceeds that of batch technology. Biomass substrate concentration is the most important input in generation of bioH2. Japan and India are the countries that benefit most from improved bioH2 technology.
article info
abstract
Article history:
This study combines a data envelopment analysis, a dynamic computable general equi-
Received 30 March 2019
librium model with estimated secondary material flows for circular economy, based on
Received in revised form
economist Joseph Schumpeter's macroeconomic theory, to develop a novel soft-link model
20 August 2019
to determine the efficiency of forty-three dark-fermentative technology of biohydrogen,
Accepted 30 August 2019
and technology improvement impacts on biohydrogen output and supply price for six
Available online xxx
major emerging Asian countries. The integrated model is found to be feasible.
Keywords:
batch technology. Biomass substrate concentration is the most important input in the
Biohydrogen
generation of biohydrogen statistically; pH influences the efficiency of the batch technol-
Dark fermentation
ogy, and the efficiency of continuous production technologies significantly exceeds that of
Data envelopment analysis (DEA)
batch technologies, but still have a gap to improve to full production efficiency for most of
Computable general equilibrium
continuous technologies. India and China generate highest output growth of biohydrogen
(CGE)
in baseline scenario. Japan and India can most benefit from improvements in batch and
Soft-link methodology
continuous biohydrogen production technology. The models and results of this study
Circular economy
provides guidelines and references for decision-makers in industry and government who
This study finds that efficiency of continuous technology significantly exceeds that of
are responsible for reforming future energy policy. © 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
E-mail address: [email protected]. https://doi.org/10.1016/j.ijhydene.2019.08.250 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Introduction The rise of the circular economy (CE) is one of the most promising economic trends that is concerned with the mitigation of the impact of climate change [1], the support of sustainable development [2], the achievement of several sustainable development goals [3], the generation of economic benefits [4] and the reduction of greenhouse gas emissions [5]. The potential of CE has motivated governments, such as EU [6,7], China [8,9], the US [10], and Japan [11], to provide roadmaps and projects to accelerate its development. Such efforts are increasingly being made by developing countries [12] and the world [13]. The CE is concerned with recycle and reuse of material in a closed loop. This core concept of CE [14] applies to urban and rural regions, industries and individuals [15,16]. Biowaste can be used to generate bioproducts [17] such as biohydrogen, in replacing the conventional production of hydrogen by reforming natural gas, which is an important secondary material in CE closed-loop. Biohydrogen is a clean, affordable, environmentally friendly form of bioenergy [18,19]; its production, especially by dark fermentation [20], is safe, economic, recyclable, close-loop, and sustainable. Biohydrogen is therefore important driven power in the transition to a CE [21]. A computable general equilibrium (CGE) model that consists of waste input-output (WIO) database is a comprehensive economy-wide optimization model with price mechanisms, resource constraints, and flexible functional forms (substitution and complementary). Such a model can be used to evaluate economic benefits in CE development policy scenarios [21]. A dynamic CGE model provides comprehensive longterm forecasts for evaluating policies for the development of the production of new forms of energy, such as biohydrogen [18,19]. This study applies a dynamic global GTAP-CGE model [22] to Asian countries, including China, India, Japan, Korea, Thailand, and Taiwan [21], because it is an adequate tool for supporting optimization decisions and evaluating the effects of policies to develop the biohydrogen industry on a CE. The concept of data envelopment analysis (DEA) was developed by Refs. [23,24] to determine the production efficiency of industry as a relative measure between input and outputs of it. It captures the relationship between inputs and outputs with respect to the maximum possible output value for a specific technology [25,26]. It can be utilized to evaluate the production efficiency of a firm, industry, national or environmental policy, or other entity [27]. DEA models are seldom used in scientific research for evaluating the efficiency of biohydrogen production by dark fermentation and pre-treatment technology in Web of Science database [28e33]. This study uses the DEA method to evaluate production efficiency of forty-three dark fermentation technologies (divided into batch and continuous technology) for biohydrogen. The goal is to determine the technological efficiency of batch and continuous technology and their improvement potential on biohydrogen industry in Asian CE economies. The foundation of soft-link methodology between DEA and CGE is theory of macroeconomic growth by innovation that
developed by the economist Joseph Schumpeter. It states that investment on intermediate goods generate technology improvement, total factor production (TFP) increase and economic growth. The Schumpeter's theory provides a bridge for linking DEA (providing technology efficiency evaluation) and CGE models (technology improvement come from investment on intermediate inputs of industry), which has rarely been done [34]. This study contributes to the literature on the evaluation of biohydrogen production efficiency by numerous dark fermentation technologies (estimated by DEA) and technology improvement potential, determine causal relations between inputs and outputs of dark fermentation biohydrogen technology, and the effects of improving the efficiency of biohydrogen production as part of the development of the CEs of Asian countries (estimated by GTAP-CGE). To this end, a new soft-linked DEA-CGE analytical methodology is developed to meet the demand for increased production efficiency and an improved biohydrogen supply chain for biohydrogen production firms. It confirms that the integrated model is feasible. It also focuses on the effects of improving biohydrogen efficiency on forecasting the biohydrogen industry growth of six major emerging Asian nations.
Model, data and scenario design DEA model This study applies Farrell's relative efficiency of production evaluation method [23,24], which names DEA to evaluate forty-three dark fermentation biohydrogen production technologies to provide current technology efficiency, then calculate modification of input/output allocation to improve current efficiency to full efficiency to be policy impacts on biohydrogen industry. Efficiency of DEA is a relative measure of the relationship between inputs and outputs of production with respect to the maximum possible output value of a given technology [23]. Farrell identified two components of institutional efficiency - technical efficiency and allocative efficiency. He defined technical efficiency as the ability to produce a certain amount of product with minimal inputs, and allocate effectiveness as the ability to absorb inputs in an optimal proportion, considering inputs costs. According to definition of production efficiency provided by Ref. [23], the technical efficiency multiple allocative efficiency will determine overall production efficiency. In his method, the effectiveness of an analyzed object, called decision-making units (DMUs), is defined as the ratio of a weighted sum of the outputs to the weighted sum of the inputs of DMUs. The simplified formula provides efficiency in terms of the production outputs of a sector divided by inputs of the sector, as follows. It can be calculated using a linear program model as [35] in Eq. (1). Production efficiency ¼ ¼
Outputs Inputs
Weighted sum of outputs u1 y1j þ u2 y2j þ … ¼ Weighted sum of iputs v1 x1j þ v2 x2j þ …
(1)
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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where u1 implies the weights to output 1, v1 implies the weights to input 1, y1j implies amounts of output 1 to unit j and x1j implies input 1 to unit j. Two kinds of production efficiency economies scales are evaluated in this study. These scales are the constant return to scale (CRS) production stage and the variable return to scale (VRS) stage [36]. The CRS stage implies the increment of inputs leads to an equivalent increase of output which implies the firms (DMUs) are now in an adequate production scale. The VRS stage implies increment of inputs leads to higher (implies firms are in the early stage of production at small production scale which cannot yet be rolled out on an adequate scale for production) or lower (implies firms are in the over-production stage at large production scale) increment of outputs. The relationship between CRS and VRS is [35] in Eq. (2): CRS ¼ VRS SE
(2)
where SE implies scale efficiency. This study will estimate three efficiencies including CRS, VRS and SE of biohydrogen production to reveal different efficiencies at different biohydrogen production scales.
CGE model In this study, the GTAP-CGE model [22], which uses a global input-output (IO) database that involves one hundred and forty regions and fifty-seven sectors in year 2011, is modified to evaluate the effects of developing biohydrogen generating and three bio-based sectors in a circular economy structure. In the GTAP model, firms optimize their production decisions by minimizing production costs subject to a production function, and households make consumption decisions by maximizing their utility function under a budget constraint. Demand and supply are balanced for all agents to yield a global equilibrium. The equations of firm's optimization are taken from Ref. [22] as shown in Eq. (3). Min: Cc;i;r ¼
X
Xc;i;r *Pc;i;r
nX or1 r i;r di;r *Xc;i;ri;r c
materials sector and raw biomass sector to generate biohydrogen to describe the circularity of biohydrogen production in the GTAP model, and an alternative input that can replace conventional hydrogen in the production of chemicals in CE modeling using GEMPACK software (as ref. [21] setting). The dynamic GTAP-CGE model can provides future comprehensively cross-section equilibrium results by years for all agents in all global regions which can not provide statistical tests between current and forecasting results.
Soft-linked model and statistical tests The soft-linked model incorporates several economic models by individually simulate impacts by one model in one time. Results of previous model then treats as new impacts fed into another model in next stage. Soft-link model for energy determines the effects of comprehensively energy related policy in recent years, which can avoid simultaneous simulation for all models in one time, which named hard-link model. The early research of soft-link methodology of CGE model is ref. [38] which reveals the effects of Australia joined APEC free trade area by a global and a national CGE model. Ref. [39,40] combined a top-down CGE (economic) model and a bottom-up linear programming (technological) model. This study uses a similar soft-link concept by a detailed technological bottom-up DEA model and a dynamic top-down GTAPCGE model to solve impacts of efficiency of dark-fermentation technology of biohydrogen, and impacts of improvement to full efficiency by DEA model on biohydrogen production in six Asian CE economies [Fig. 1]. Several statistical tests are conducted after the DEA efficiency is evaluated; they include Tobit regression, Granger causality test, Kolmogorov-Smirnov Normality test, and Mean tests. These tests determine the causal relationships between inputs and outputs, CRS and VRS efficiency of biohydrogen, which yield a reliable statistical relationship when DEA and CGE models are combined.
Data for DEA model
c
s:t: Yi;r ¼ Ai;r
3
(3)
X 1 di;r ¼ 1: 1 di;r 0; di;r ¼ 1 þ ri;r c where Cc;i;r , Xc;i;r , and Pc;i;r represent the cost of production, and the quantity and the price of input c for sector i in region r; and Yi;r , Ai;r , di;r and ri;r represent the production value, technology, substitution elasticity and parameter for sector i in region r. The solutions of optimization are obtained by Largrangian method then be linearized to obtain demand function of firm's products. The major concepts associated with the CE are secondary materials and a closed-loop cycle of materials [37] which is common model settings with building secondary material flow into a database in CE related articles (as ref. [21] mentioned). In this study, data on the flow of secondary materials was built as a “Waste” sector with complete supply and sales chains in GTAP database [21]. Then, substitutions mechanism in GTAP was modified between the secondary
Numerous studies mention lots of advantages of dark fermentation technology to produce biohydrogen in articles, consequently to estimate the production efficiency for different batch and continuous technologies of biohydrogen production is important for supporting scientists and engineers to figure out which technology is adequate to utilize to produce biohydrogen efficiently. Batch technology tests are unsteady state tests with hydrogen quantity produced from a fixed amount of feed substrate increasing with time, while continuous technology tests are commonly steady state tests with fixed feeding rate of substrate and constant hydrogen production rate in bioreactors. Considerable results in different experimental circumstances were provided in articles for two modes. Ref. [41] reviewed all required input data (including pH, temperature ( C), substrate concentration (g COD/L)) and output data (peak hydrogen production rate, HPR (L/L/d), and hydrogen yield, HY (mol/mol-sugar)) of batch and continuous biohydrogen production which required for DEA model estimation in this study. The peak hydrogen production rate (HPR) data can be yielded by fitting the kinetic data with empirical model such as modified
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Scenario I: Data Envelopment Analysis (DEA) Biohydrogen technology CRS and VRS, SE
Efficiency of batch tech.
Efficiency of continuous
of dark fermentation
tech. of dark fermentation
Scenario II: Statistical Analysis Tobit model Granger Causality Test Kolmogorov-Smirnov Normality Test
Soft-link Methodology: Efficiency improvement of scenario I to full efficiency (equals to unity)
Non-parametric Medium Test
Scenario III: Global Trade Analysis Project (GTAP) Model Effects of efficiency improvement of biohydrogen industry in six Asian regions with circular economy Fig. 1 e Soft-link integrated DEA-CGE model and scenario design.
Gompertz equation (eq. (1) in Ref. [42] in which the peak production rate of hydrogen is a fitting parameter. Although there exists ambiguity of definitions of the peak production rate reported in literature, this study adopts the HPR data instead of HY from Ref. [41] for DEA model estimation, and the major conclusions obtained in this study would remain unchanged since only the minor data corrections would occur in calculations. The input and output data with same units of batch (mass and onetime production) (Table 1) and continuous (Table 2) biohydrogen generation technology thus obtained. The production efficiency of DEA is standardized to unit which is easier to compare among different technologies in same basis.
Data for GTAP model China, India, Japan, South Korea, Malaysia and Taiwan are treated separately, while one hundred and thirty-four other
countries are aggregated as the “rest of world” in GTAP database. The fifty-seven sectors in the GTAP database are aggregated to thirty-one sectors, and the “Waste” sector and four bio-based sectors related to biohydrogen, bioplastic, biopharmaceuticals and genetically modified organisms are added. The final database includes seven regions and thirtysix sectors, along with model modification programming with CE mechanism. The output value of the waste sector is calculated as US$ 328.59 billion for China, US$ 79.63 billion for India, US$ 268.98 billion for Japan, US$ 52.53 billion for South Korea, US$ 13.02 billion for Malaysia, US$ 21.22 billion for Taiwan, US$ 1688.78 billion for the rest of the world, and US$2452.84 billion globally [4,21,43]. The production shares and sales shares of the waste and four bio-based sectors in the six regions are estimated and mapped to related GTAP sectors, for which supply and sales chains are constructed [21].
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 1 e The inputs and outputs of batch dark fermentation technology of biohydrogen production. Wastewater type
Sugar rich Cassava WW Rice mill WW Rice mill WW Rice mill WW CMS CMS CMS CMS CMS BWW BWW BWW BWW Sugar beet juice Distillery WW Dairy WW Dairy WW Complex dairy WW Organic WW Toxic/industrial Raw plastic Physico-chemical treated plastic industry Toilet aircraft Olive mill WW TWW CMS
Inoculum source
pH
Temperature ( C)
Substrate concentration (g COD/L)
Peak hydrogen production rate (HPR) (L/L/d)
Clostridium acetobutylicum ATCC824 Enterobacter aerogens Citrobacter ferundii Enterobacter aerogens RM08 Clostridium tyrobutyricum Clostridium pasteurianum Clostridium sporosphaeroides Clostridium pasteurianum þ Clostridium sporosphaeroides Clostridium tyrobutyricum þ Clostridium sporosphaeroides EMC-compost EMC þ E. Coli XL-1 BLUE EMC þ E. Cloacae EMC þ E. Coli XL1 blue Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Soil Anaerobic sludge Anaerobic sludge
7.0
36
17.5
0.60
6.5 6.5 6.5 7.0 7.0 7.0 7.0
33 33 33 35 35 35 35
15.8 15.8 16.1 50 50 50 40
1.72 1.61 1.71 1.48 3.69 0.48 2.97
7.0
35
60
0.62
5.5 5.5 5.5 6.5 5.5 5.5 5.5 5.5 5.5 5.0 5.75 5.5
37 37 37 37 37 37 37 37 37 30 36 36
10 10 25.8 5 4 34.8 15 15.3 56 10.5 3 3
1.81 1.25 3.04 1.75 3.2 2.88 1.45 2.31 0.89 0.32 0.28 0.1
Anaerobic sludge Anaerobic sludge
5.5 7.0 7.0 6.0
36 37 37 35
2 50 20 40
0.28 0.42 4.32 1.5
Anaerobic sludge
Source. Ref. [41] and modified by this study. Note. All notations come from Ref. [41]. BWW implies the “beverage wastewater”, CMS implies the “condensed soluble molasses”, WW implies the “Wastewater”.
A historical simulation of GTAP was carried out using historical economic data to generate databases from 2012 to 2017. To consider advances in biohydrogen dark fermentation technology, which is a rapid production technology, the impact shocks that are fed into GTAP model from the DEA model to determine when the lower production rate of biohydrogen technology to keep up with the benchmark technology (full production efficiency, implies efficiency equals to one) in the near future. Baseline forecasting from 2018 to 2022 was conducted by considering several exogenous shocks, including an increase in the global import prices of primary energy of 3.7415% per year, and by using population forecasts of the seven regions, which were obtained from the IMF website. The rates of technological improvement in the development of CRS and VRS scenarios for the biohydrogen sectors are set to 0.168575% and 0.191016% each year, respectively [18].
Scenario I. Evaluating DEA efficiency of CRS and VRS of forty-three dark fermentation (batch and continuous together) technologies of biohydrogen production. Scenario II. Performing the Tobit regression, Granger causality test, Kolmogorov-Smirnov normality distribution test, and Median tests to determine causality relationship of inputs and outputs of biohydrogen production. Scenario III. Calculating mean of technology efficiency generated by DEA from low efficient technologies (efficiency is less than 1) to benchmark technologies (efficiency equals to 1) then fed into dynamic GTAP model to reveal impacts on output value and supply price of biohydrogen in six Asian countries in CE.
Results and discussions Scenario I
Scenario design Scenarios specify the link between DEA and CGE. Several statistical tests are performed to completely describe the concepts built in this study. The designed scenarios are as follows.
Table 3 presents results concerning the CRS-DEA efficiencies of batch and continuous biohydrogen technology. The most efficient batch technology uses anaerobic sludge as an inoculum source and sugar beet juice as wastewater, providing a efficiency of 0.224, implying that a production efficiency
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 2 e The inputs and outputs of continuous dark fermentation technology of biohydrogen production. Inoculum
Indigenous microflora ADS AS AS ADS ADS AS Mixed culture ADS ADS ADS AS AS AS AS AS AS EMC
Waste water
Sweet sorghum extract Palm oil mill effluent Condensed molasses Condensed molasses Tofu processing WW Desugared molasses Sugarecane juice Soft-drink WW Cheese whey Tapioca WW Desugared molasses Tofu processing WW Sweet sorghum Dephenolized olive mill WW Molasses Sugary WW Beet sugar WW Beverage WW
Reactor mode
CSTR ASBR CSTR ICBR MBR UASB CSTR UAPBR UASB ABR UASB CSTR ASBR PBR CSTR CSTR CSTR ICBR
Substrate concentration (g COD/L)
pH
Temperature ( C)
HRT (h)
Rs
Rs
Rs
Rs
15.44 20.00 40.00 40.00 43.40 16.70 25.00 1.94 20.00 16.15 16.70 20.00 30.00 38.79 8.00 6.00 15.00 20.00
5.60 6.75 5.50 5.50 5.50 5.50 6.00 6.20 5.70 9.00 5.60 5.50 5.00 7.03 4.35 4.50 4.30 6.00
35.00 37.00 37.00 37.00 60.00 55.00 37.00 25.00 23.50 33.05 55.00 35.00 31.50 35.00 35.00 35.00 35.00 35.00
12.00 66.00 4.25 4.25 5.00 24.00 20.00 0.50 15.00 13.50 16.00 15.00 24.00 96.00 7.00 8.00 8.00 4.75
HPR (L/L/d)
2.93 6.70 14.04 7.60 19.86 4.50 2.29 9.60 8.64 0.83 5.60 1.73 3.20 7.00 7.47 3.45 10.80 55.40
Source. Ref. [41] and modified by this study. Note. All notations come from Ref. [41]. ADS implies “anaerobic digester sludge”; AS implies “anaerobic sewage sludge”; EMC implies “Enriched mixed culture”; CSTR implies “continuously stirred tank reactor; ASBR implies “anaerobic sequencing batch reactor”; ICBR implies “immobilized cell bioreactor”; MBR implies “membrane bioreactor”; UASB implies “upflow anaerobic blanket reactor”; UAPBR implies “upflow anaerobic packed bed reactor”; ABR implies “anaerobic baffled reactor”; PBR implies “packed bed reactor”; .
improvement potential of 77.6% (1 minus 0.224) will enable the anaerobic sludge-sugar beet juice batch technology to keep up with the full efficient production technology (efficiency is equals to 1 (100%)) which are mixed culture with soft-drink wastewater and EMC with beverage wastewater (continuous technology) in these forty-three dark fermentation technologies. The mean of efficiencies of the CRS of batch and continuous technologies are 0.045 and 0.251 respectively implying that the efficiency of continuous technology considerably exceeds 5.78 times (¼0.251/0.045) that of batch technology, and these two technologies have much room for future improvement (batch technology has improvement potential for 25 times, and continuous technology has improvement potential for 4 times, when their efficiency improve to 1). Results of efficiency of VRS-DEA exceed those of CRS because some DMUs did not reach their optimal economic production scale. The constraints on CRS are stricter than those on VRS and differences are caused by adequate production scale efficiency (SE). Also, for the most efficient batch technology (anaerobic sludge with sugar beet juice), SE is 0.569, implying that this technology improvement potential can reach an optimal production scale (SE equals to 1) by 43.1% (1 minus 0.569), as a result by increasing biohydrogen production output or resource allocation.
Scenario II Tables 4e7 presents the results of statistical tests of the CRSDEA efficiencies of batch and continuous biohydrogen technologies. Since fewer than 30 data points are available, their
probability distribution may not be normal so non-parametric statistical tests may be used to prevent statistical bias. The results of all followed tests will demonstrate the real impacts of the inputs on biohydrogen generation. First, the range of efficiencies that are obtained using DEA is standardized to zero to one, which enable the Tobit regression model to be used (independent variable of Tobit model is accumulated probability also between zero and unity) to determine the causal relationships between production efficiency (independent variable) and pH, temperature, substrate concentration and hydrogen production rate (HPR) (dependent variables). The results indicate that the substrate concentration (p-value < 0.05) and HPR (p-value < 0.01) significantly affect production efficiency statistically. HPR is hydrogen output, which directly affects production efficiency. Substrate concentration is also an important factor that influences the biohydrogen production efficiency (in Table 4 for CRS). Second, Granger causality test is conducted to determine pairwise relationships between inputs and outputs of biohydrogen production. Most of the causality tests do not enable the null hypothesis (H0) to be rejected (Table 5), so the causal relationships between the inputs and outputs of biohydrogen generation technology cannot be identified. The Kolmogorov-Smirnov normality test is carried out to test the probability distribution is normal or not. The results in Table 6 reveal that the inputs and DEA efficiency of the biohydrogen are not normally distributed so a parametric method such as ANOVA analysis cannot be used to test the mean impacts of various inputs and efficiency between two biohydrogen production technologies.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 3 e Biohydrogen production efficiency of batch and continuous technology. Batch biohydrogen production Inoculum source
Wastewater type
CRS
VRS
SE
Sugar rich Cassava WW Rice mill WW Rice mill WW Rice mill WW CMS CMS CMS CMS
0.012 0.038 0.036 0.038 0.027 0.067 0.009 0.054
0.012 0.038 0.036 0.038 0.027 0.067 0.009 0.054
0.991 0.999 0.999 0.999 1.000 1.000 1.000 1.000
CMS
0.011
0.011
1.000
BWW BWW BWW BWW Sugar beet juice Distillery WW Dairy WW Dairy WW Complex dairy WW Organic WW Toxic/industrial Raw plastic Physico-chemical treated plastic industry Toilet aircraft Olive mill WW TWW CMS
0.061 0.042 0.060 0.099 0.224 0.057 0.034 0.054 0.018 0.010 0.023 0.008
0.075 0.052 0.072 0.101 0.393 0.068 0.039 0.061 0.021 1.000 0.035 0.022
0.813 0.813 0.833 0.986 0.569 0.833 0.881 0.884 0.833 0.010 0.661 0.382
0.030 0.007 0.077 0.027 0.045
1.000 0.008 0.078 0.027 0.134
0.030 0.946 0.993 1.000 0.818
Inoculum Source
Wastewater type
CRS
VRS
SE
Indigenous microflora ADS AS AS ADS ADS AS Mixed culture ADS ADS ADS AS AS AS AS AS AS EMC Average
Sweet sorghum extract Palm oil mill effluent Condensed molasses Condensed molasses Tofu processing WW Desugared molasses Sugarcane juice Soft-drink WW Cheese whey Tapioca WW Desugared molasses Tofu processing WW Sweet sorghum Dephenolized olive mill WW Molasses Sugary WW Beet sugar WW Beverage WW
0.067 0.120 0.276 0.150 0.391 0.097 0.041 1.000 0.232 0.018 0.120 0.034 0.069 0.126 0.316 0.185 0.272 1.000 0.251
0.074 0.121 0.332 0.180 0.470 0.110 0.041 1.000 1.000 0.018 0.131 0.041 0.185 0.126 1.000 1.000 1.000 1.000 0.435
0.906 0.993 0.833 0.833 0.833 0.881 1.000 1.000 0.232 0.999 0.915 0.833 0.375 1.000 0.316 0.185 0.272 1.000 0.745
Clostridium acetobutylicum ATCC824 Enterobacter aerogens Citrobacter ferundii Enterobacter aerogens RM08 Clostridium tyrobutyricum Clostridium pasteurianum Clostridium sporosphaeroides Clostridium pasteurianum þ Clostridium sporosphaeroides Clostridium tyrobutyricum þ Clostridium sporosphaeroides EMC-compost EMC þ E. Coli XL-1 BLUE EMC þ E. Cloacae EMC þ E. Coli XL1 blue Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Soil Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Average Continuous biohydrogen production
Note. All abbreviations are as in notes in Tables 1 and 2. TWW implies “textile wastewater”.
Based on the results in Table 7, non-parametric tests are performed to determine whether the efficiencies and inputs of batch and continuous production technology are similar or significantly different. The results demonstrate that the median efficiencies of batch and continuous biohydrogen production technology are significantly different with each other, but other inputs have almost the same medians, implying that the efficiency and HPR of
biohydrogen generation are determined by the production method (batch or continuous), rather than not by inputs (which are similar). According to the test results for scenario II, substrate concentration is the most important biohydrogen generation input; pH influences the efficiency of batch technology; and the efficiency and HPR of continuous production are significantly different (greater) than those of batch technology.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 4 e Tobit model’s causality relationships estimation of CRS efficiency among pH, temperature, substrate concentration and HPR. Batch production technology Variable Constant pH Temperature Substrate concentration HPR
Coefficient 0.096 0.003 0.004 0.001 0.024
Standard Error 0.141 0.010 0.003 0.000 0.005
P-value 0.497 0.768 0.256 0.037** 0.000***
Continuous production technology Variable
Coefficient
Standard Error
P-value
Constant pH Temperature Substrate concentration HPR
0.421 0.004 0.005 0.007 0.019
0.269 0.036 0.004 0.003 0.003
0.117 0.901 0.283 0.034** 0.000***
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
Scenario III The efficiency improvements to benchmark technology efficiency of the biohydrogen sector in six Asian countries, obtained by DEA, are fed into the GTAP-CGE model for batch and continuous biohydrogen generation technology, as shown in Table 8. Baseline results, which imply the driven power of development of biohydrogen industry without policy support
Table 6 e KeS Normality tests for CRS/VRS and inputs in DEA simulation. Null Hypothesis (H0)
P-value
Decision
CRS’s distribution is normal distribution VRS’s distribution is normal distribution pH’s distribution is normal distribution Temperature’s distribution is normal distribution Substrate’s distribution is normal distribution HPR’s distribution is normal distribution
0.000*** 0.000*** 0.001*** 0.000***
Reject Reject Reject Reject
0.000***
Reject H0
0.000***
Reject H0
H0 H0 H0 H0
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
and external technology improvement, reveals that the level of output quantity of the biohydrogen industry are high in India (around 8%) and China (around 6%), followed by Korea (around 4.66%e4.82%), Malaysia (around 4%), Taiwan (under 3%) and Japan (around 1%). Level results of batch and continuous technology are slightly higher than baseline but is less than 2 decimal places (make results indifferent in upper part of Table 8). The real effects of increasing production efficiency for batch and continuous technology from baseline efficiency on output quantity of biohydrogen should be counted by results of batch minus results of baseline, and results of continuous minus results of baseline (in bottom of Table 8). The real effects of two technologies are slightly increasing in all countries, Japan (0.016% and 0.015%) and India (0.01% and 0.008%) are top two countries. Japan and India are the two countries that most benefit from improvements in the efficiencies of batch and
Table 5 e Granger causality test of CRS-DEA efficiency among HPR, pH, temperature and substrate concentration. Batch production technology Null Hypothesis (H0) HPR does not Granger Cause CRS CRS does not Granger Cause HPR pH does not Granger Cause CRS CRS does not Granger Cause PH T does not Granger Cause CRS CRS does not Granger Cause T SUB does not Granger Cause CRS CRS does not Granger Cause SUB VRS does not Granger Cause CRS CRS does not Granger Cause VRS pH does not Granger Cause HPR HPR does not Granger Cause PH T does not Granger Cause HPR HPR does not Granger Cause T SUB does not Granger Cause HPR HPR does not Granger Cause SUB T does not Granger Cause PH pH does not Granger Cause T SUB does not Granger Cause PH pH does not Granger Cause SUB SUB does not Granger Cause T T does not Granger Cause SUB
Continuous production technology F-Statistic
P-value
Null Hypothesis (H0)
F-Statistic
P-value
2.55 0.57 2.90 0.59 0.45 0.19 0.01 1.56 0.31 0.31 2.16 1.46 0.51 0.11 0.73 1.95 1.29 0.35 0.33 1.93 1.46 0.08*
0.11 0.58 0.08* 0.57 0.64 0.83 0.99 0.24 0.73 0.73 0.14 0.26 0.61 0.89 0.49 0.17 0.30 0.71 0.72 0.17 0.26 0.92
HPR does not Granger Cause CRS CRS does not Granger Cause HPR pH does not Granger Cause CRS CRS does not Granger Cause PH T does not Granger Cause CRS CRS does not Granger Cause T SUB does not Granger Cause CRS CRS does not Granger Cause SUB VRS does not Granger Cause CRS CRS does not Granger Cause VRS pH does not Granger Cause HPR HPR does not Granger Cause PH T does not Granger Cause HPR HPR does not Granger Cause T SUB does not Granger Cause HPR HPR does not Granger Cause SUB T does not Granger Cause PH pH does not Granger Cause T SUB does not Granger Cause PH pH does not Granger Cause SUB SUB does not Granger Cause T T does not Granger Cause SUB
0.13 0.04 1.79 4.66 0.76 0.51 0.63 0.06 1.11 2.45 2.67 0.10 0.23 3.43 0.59 0.13 0.54 1.54 1.26 0.07 2.71 0.10
0.88 0.96 0.21 0.03** 0.49 0.61 0.55 0.94 0.36 0.13 0.11 0.91 0.80 0.07* 0.57 0.88 0.60 0.26 0.32 0.94 0.11 0.91
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 7 e Median test of efficiency of batch and continuous biohydrogen production technology. Null Hypothesis (H0)
P-value
CRS’s median between batch and continuous tech. are same VRS’s median between batch and continuous tech. are same pH’s median between batch and continuous tech. are same Temperature’s median between batch and continuous tech. are same Substrate’s median between batch and continuous tech. are same HPR’s median between batch and continuous tech. are same
0.000*** Reject H0
Decision
0.000*** Reject H0 0.425
Do not reject H0
0.983
Do not reject H0
0.661
Do not reject H0
0.000*** Reject H0
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
continuous biohydrogen production technology. An annual decrease in the supply price of biohydrogen also favors the development of biohydrogen by reducing the costs of those who use biohydrogen. The efficiency of batch technology is lower than that of continuous technology, and the former requires more efficiency improvement to reach optimal production conditions, which will generate more benefit for the biohydrogen industry and upstream and downstream supply chains.
Conclusion In this work, a new soft-link methodology that combines DEA and CGE models is developed to incorporate both production efficiency and technological improvements and their benefits on biohydrogen production in six Asian CE regions. Input and output data for batch and continuous biohydrogen production
Table 8 e Efficiency improvement of DEA induces development of biohydrogen sector by dynamic GTAP model in six Asian CE countries. Output quantity of biohydrogen
Supply price of biohydrogen
Baseline (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.71 0.39 4.78 3.70 3.00
6.57 8.52 0.50 4.70 4.00 2.89
6.17 8.29 0.71 4.66 4.24 2.76
5.73 8.02 0.99 4.63 4.44 2.63
5.52 7.94 1.37 4.82 4.73 2.81
Baseline (level) China India Japan Korea Malaysia Taiwan
2018
2019
2020
2021
2022
0.69 1.49 1.58 1.37 0.04 1.33
0.63 1.29 1.59 1.52 0.13 1.49
0.57 1.06 1.55 1.66 0.19 1.64
0.51 0.81 1.48 1.78 0.25 1.79
0.45 0.52 1.53 1.95 0.27 1.97
Batch (level)
2018
2019
2020
2021
2022
Batch (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.72 0.41 4.79 3.71 3.01
6.57 8.53 0.51 4.71 4.01 2.90
6.17 8.30 0.73 4.67 4.25 2.77
5.73 8.03 1.01 4.64 4.45 2.64
5.52 7.95 1.39 4.83 4.73 2.82
China India Japan Korea Malaysia Taiwan
0.88 1.68 1.39 1.17 0.24 1.14
0.82 1.48 1.40 1.33 0.32 1.30
0.76 1.25 1.36 1.47 0.39 1.45
0.70 1.00 1.29 1.59 0.44 1.60
0.64 0.71 1.33 1.76 0.46 1.78
Continuous (level)
2018
2019
2020
2021
2022
Continuous (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.72 0.41 4.78 3.71 3.01
6.57 8.53 0.51 4.71 4.01 2.9
6.17 8.3 0.73 4.67 4.25 2.77
5.73 8.03 1.01 4.64 4.44 2.64
5.52 7.95 1.39 4.83 4.73 2.82
China India Japan Korea Malaysia Taiwan
0.86 1.66 1.41 1.20 0.21 1.16
0.80 1.46 1.42 1.35 0.30 1.32
0.74 1.22 1.38 1.49 0.36 1.47
0.68 0.98 1.31 1.62 0.42 1.62
0.61 0.69 1.36 1.78 0.44 1.80
Batch minus baseline (difference)
2018
2019
2020
2021
2022
Batch minus baseline (difference)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
0.003 0.011 0.016 0.009 0.007 0.010
0.002 0.010 0.016 0.009 0.007 0.010
0.002 0.009 0.016 0.008 0.007 0.010
0.002 0.008 0.016 0.008 0.007 0.009
0.001 0.006 0.016 0.007 0.007 0.009
China India Japan Korea Malaysia Taiwan
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.191
0.191 0.190 0.192 0.191 0.192 0.191
Conti. minus baseline (difference)
2018
2019
2020
2021
2022
Conti. minus baseline (difference)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
0.003 0.010 0.014 0.008 0.006 0.009
0.002 0.009 0.015 0.008 0.006 0.009
0.002 0.008 0.015 0.007 0.006 0.008
0.002 0.007 0.015 0.007 0.006 0.008
0.001 0.006 0.015 0.007 0.006 0.008
China India Japan Korea Malaysia Taiwan
0.169 0.169 0.169 0.169 0.170 0.169
0.169 0.169 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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technologies are collected and modified, and efficiencies of forty-three technologies are estimated. Results found that the improvement potentials of two technologies leave much to be desired. The production efficiency of continuous technology is higher than batch technology which implies if firms adopt continuous technology to produce biohydrogen will obtain higher output quantity or lower cost. The supply price of biohydrogen will decrease (around 0.168% to 0.192%) when the batch and continuous technologies improve from baseline efficiency to full production efficiency. Biomass substrate concentration is found to be the most important input of biohydrogen generation, pH influences the efficiency of batch technology, and the efficiency and HPR of continuous production are significantly greater than those of batch technology. India and China are top two countries which generate highest output growth of biohydrogen in baseline scenario, but Japan and India are the two countries that most benefit from improvements in the efficiencies of batch and continuous biohydrogen production technology. Results reveals that the basic driven power in current production efficiency of biohydrogen is high in most of Asian countries, but the technology improvement generates slightly positive impact of increase biohydrogen output quantity because of the competition of other hydrogen sources and renewable energies. Efficiency improvements also favor the development of the biohydrogen industry and its supply chain, the development of CE and the bioeconomy. Results verify that the DEA-CGE model that is developed herein is workable. The models and results in this work provide guidelines and references for decision-maker in industry and government who are responsible for reforming future energy policy.
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Efficiency and economic benefit of darkfermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE) Duu-Hwa Lee Institute of Applied Economics, National Taiwan Ocean University, Keelung, Taiwan
highlights A soft-link DEA-CGE model was developed, and its effectiveness confirmed. Efficiency and improvement potential of dark fermentation technologies are provided. Efficiency of continuous technology significantly exceeds that of batch technology. Biomass substrate concentration is the most important input in generation of bioH2. Japan and India are the countries that benefit most from improved bioH2 technology.
article info
abstract
Article history:
This study combines a data envelopment analysis, a dynamic computable general equi-
Received 30 March 2019
librium model with estimated secondary material flows for circular economy, based on
Received in revised form
economist Joseph Schumpeter's macroeconomic theory, to develop a novel soft-link model
20 August 2019
to determine the efficiency of forty-three dark-fermentative technology of biohydrogen,
Accepted 30 August 2019
and technology improvement impacts on biohydrogen output and supply price for six
Available online xxx
major emerging Asian countries. The integrated model is found to be feasible.
Keywords:
batch technology. Biomass substrate concentration is the most important input in the
Biohydrogen
generation of biohydrogen statistically; pH influences the efficiency of the batch technol-
Dark fermentation
ogy, and the efficiency of continuous production technologies significantly exceeds that of
Data envelopment analysis (DEA)
batch technologies, but still have a gap to improve to full production efficiency for most of
Computable general equilibrium
continuous technologies. India and China generate highest output growth of biohydrogen
(CGE)
in baseline scenario. Japan and India can most benefit from improvements in batch and
Soft-link methodology
continuous biohydrogen production technology. The models and results of this study
Circular economy
provides guidelines and references for decision-makers in industry and government who
This study finds that efficiency of continuous technology significantly exceeds that of
are responsible for reforming future energy policy. © 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
E-mail address: [email protected]. https://doi.org/10.1016/j.ijhydene.2019.08.250 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
2
international journal of hydrogen energy xxx (xxxx) xxx
Introduction The rise of the circular economy (CE) is one of the most promising economic trends that is concerned with the mitigation of the impact of climate change [1], the support of sustainable development [2], the achievement of several sustainable development goals [3], the generation of economic benefits [4] and the reduction of greenhouse gas emissions [5]. The potential of CE has motivated governments, such as EU [6,7], China [8,9], the US [10], and Japan [11], to provide roadmaps and projects to accelerate its development. Such efforts are increasingly being made by developing countries [12] and the world [13]. The CE is concerned with recycle and reuse of material in a closed loop. This core concept of CE [14] applies to urban and rural regions, industries and individuals [15,16]. Biowaste can be used to generate bioproducts [17] such as biohydrogen, in replacing the conventional production of hydrogen by reforming natural gas, which is an important secondary material in CE closed-loop. Biohydrogen is a clean, affordable, environmentally friendly form of bioenergy [18,19]; its production, especially by dark fermentation [20], is safe, economic, recyclable, close-loop, and sustainable. Biohydrogen is therefore important driven power in the transition to a CE [21]. A computable general equilibrium (CGE) model that consists of waste input-output (WIO) database is a comprehensive economy-wide optimization model with price mechanisms, resource constraints, and flexible functional forms (substitution and complementary). Such a model can be used to evaluate economic benefits in CE development policy scenarios [21]. A dynamic CGE model provides comprehensive longterm forecasts for evaluating policies for the development of the production of new forms of energy, such as biohydrogen [18,19]. This study applies a dynamic global GTAP-CGE model [22] to Asian countries, including China, India, Japan, Korea, Thailand, and Taiwan [21], because it is an adequate tool for supporting optimization decisions and evaluating the effects of policies to develop the biohydrogen industry on a CE. The concept of data envelopment analysis (DEA) was developed by Refs. [23,24] to determine the production efficiency of industry as a relative measure between input and outputs of it. It captures the relationship between inputs and outputs with respect to the maximum possible output value for a specific technology [25,26]. It can be utilized to evaluate the production efficiency of a firm, industry, national or environmental policy, or other entity [27]. DEA models are seldom used in scientific research for evaluating the efficiency of biohydrogen production by dark fermentation and pre-treatment technology in Web of Science database [28e33]. This study uses the DEA method to evaluate production efficiency of forty-three dark fermentation technologies (divided into batch and continuous technology) for biohydrogen. The goal is to determine the technological efficiency of batch and continuous technology and their improvement potential on biohydrogen industry in Asian CE economies. The foundation of soft-link methodology between DEA and CGE is theory of macroeconomic growth by innovation that
developed by the economist Joseph Schumpeter. It states that investment on intermediate goods generate technology improvement, total factor production (TFP) increase and economic growth. The Schumpeter's theory provides a bridge for linking DEA (providing technology efficiency evaluation) and CGE models (technology improvement come from investment on intermediate inputs of industry), which has rarely been done [34]. This study contributes to the literature on the evaluation of biohydrogen production efficiency by numerous dark fermentation technologies (estimated by DEA) and technology improvement potential, determine causal relations between inputs and outputs of dark fermentation biohydrogen technology, and the effects of improving the efficiency of biohydrogen production as part of the development of the CEs of Asian countries (estimated by GTAP-CGE). To this end, a new soft-linked DEA-CGE analytical methodology is developed to meet the demand for increased production efficiency and an improved biohydrogen supply chain for biohydrogen production firms. It confirms that the integrated model is feasible. It also focuses on the effects of improving biohydrogen efficiency on forecasting the biohydrogen industry growth of six major emerging Asian nations.
Model, data and scenario design DEA model This study applies Farrell's relative efficiency of production evaluation method [23,24], which names DEA to evaluate forty-three dark fermentation biohydrogen production technologies to provide current technology efficiency, then calculate modification of input/output allocation to improve current efficiency to full efficiency to be policy impacts on biohydrogen industry. Efficiency of DEA is a relative measure of the relationship between inputs and outputs of production with respect to the maximum possible output value of a given technology [23]. Farrell identified two components of institutional efficiency - technical efficiency and allocative efficiency. He defined technical efficiency as the ability to produce a certain amount of product with minimal inputs, and allocate effectiveness as the ability to absorb inputs in an optimal proportion, considering inputs costs. According to definition of production efficiency provided by Ref. [23], the technical efficiency multiple allocative efficiency will determine overall production efficiency. In his method, the effectiveness of an analyzed object, called decision-making units (DMUs), is defined as the ratio of a weighted sum of the outputs to the weighted sum of the inputs of DMUs. The simplified formula provides efficiency in terms of the production outputs of a sector divided by inputs of the sector, as follows. It can be calculated using a linear program model as [35] in Eq. (1). Production efficiency ¼ ¼
Outputs Inputs
Weighted sum of outputs u1 y1j þ u2 y2j þ … ¼ Weighted sum of iputs v1 x1j þ v2 x2j þ …
(1)
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
international journal of hydrogen energy xxx (xxxx) xxx
where u1 implies the weights to output 1, v1 implies the weights to input 1, y1j implies amounts of output 1 to unit j and x1j implies input 1 to unit j. Two kinds of production efficiency economies scales are evaluated in this study. These scales are the constant return to scale (CRS) production stage and the variable return to scale (VRS) stage [36]. The CRS stage implies the increment of inputs leads to an equivalent increase of output which implies the firms (DMUs) are now in an adequate production scale. The VRS stage implies increment of inputs leads to higher (implies firms are in the early stage of production at small production scale which cannot yet be rolled out on an adequate scale for production) or lower (implies firms are in the over-production stage at large production scale) increment of outputs. The relationship between CRS and VRS is [35] in Eq. (2): CRS ¼ VRS SE
(2)
where SE implies scale efficiency. This study will estimate three efficiencies including CRS, VRS and SE of biohydrogen production to reveal different efficiencies at different biohydrogen production scales.
CGE model In this study, the GTAP-CGE model [22], which uses a global input-output (IO) database that involves one hundred and forty regions and fifty-seven sectors in year 2011, is modified to evaluate the effects of developing biohydrogen generating and three bio-based sectors in a circular economy structure. In the GTAP model, firms optimize their production decisions by minimizing production costs subject to a production function, and households make consumption decisions by maximizing their utility function under a budget constraint. Demand and supply are balanced for all agents to yield a global equilibrium. The equations of firm's optimization are taken from Ref. [22] as shown in Eq. (3). Min: Cc;i;r ¼
X
Xc;i;r *Pc;i;r
nX or1 r i;r di;r *Xc;i;ri;r c
materials sector and raw biomass sector to generate biohydrogen to describe the circularity of biohydrogen production in the GTAP model, and an alternative input that can replace conventional hydrogen in the production of chemicals in CE modeling using GEMPACK software (as ref. [21] setting). The dynamic GTAP-CGE model can provides future comprehensively cross-section equilibrium results by years for all agents in all global regions which can not provide statistical tests between current and forecasting results.
Soft-linked model and statistical tests The soft-linked model incorporates several economic models by individually simulate impacts by one model in one time. Results of previous model then treats as new impacts fed into another model in next stage. Soft-link model for energy determines the effects of comprehensively energy related policy in recent years, which can avoid simultaneous simulation for all models in one time, which named hard-link model. The early research of soft-link methodology of CGE model is ref. [38] which reveals the effects of Australia joined APEC free trade area by a global and a national CGE model. Ref. [39,40] combined a top-down CGE (economic) model and a bottom-up linear programming (technological) model. This study uses a similar soft-link concept by a detailed technological bottom-up DEA model and a dynamic top-down GTAPCGE model to solve impacts of efficiency of dark-fermentation technology of biohydrogen, and impacts of improvement to full efficiency by DEA model on biohydrogen production in six Asian CE economies [Fig. 1]. Several statistical tests are conducted after the DEA efficiency is evaluated; they include Tobit regression, Granger causality test, Kolmogorov-Smirnov Normality test, and Mean tests. These tests determine the causal relationships between inputs and outputs, CRS and VRS efficiency of biohydrogen, which yield a reliable statistical relationship when DEA and CGE models are combined.
Data for DEA model
c
s:t: Yi;r ¼ Ai;r
3
(3)
X 1 di;r ¼ 1: 1 di;r 0; di;r ¼ 1 þ ri;r c where Cc;i;r , Xc;i;r , and Pc;i;r represent the cost of production, and the quantity and the price of input c for sector i in region r; and Yi;r , Ai;r , di;r and ri;r represent the production value, technology, substitution elasticity and parameter for sector i in region r. The solutions of optimization are obtained by Largrangian method then be linearized to obtain demand function of firm's products. The major concepts associated with the CE are secondary materials and a closed-loop cycle of materials [37] which is common model settings with building secondary material flow into a database in CE related articles (as ref. [21] mentioned). In this study, data on the flow of secondary materials was built as a “Waste” sector with complete supply and sales chains in GTAP database [21]. Then, substitutions mechanism in GTAP was modified between the secondary
Numerous studies mention lots of advantages of dark fermentation technology to produce biohydrogen in articles, consequently to estimate the production efficiency for different batch and continuous technologies of biohydrogen production is important for supporting scientists and engineers to figure out which technology is adequate to utilize to produce biohydrogen efficiently. Batch technology tests are unsteady state tests with hydrogen quantity produced from a fixed amount of feed substrate increasing with time, while continuous technology tests are commonly steady state tests with fixed feeding rate of substrate and constant hydrogen production rate in bioreactors. Considerable results in different experimental circumstances were provided in articles for two modes. Ref. [41] reviewed all required input data (including pH, temperature ( C), substrate concentration (g COD/L)) and output data (peak hydrogen production rate, HPR (L/L/d), and hydrogen yield, HY (mol/mol-sugar)) of batch and continuous biohydrogen production which required for DEA model estimation in this study. The peak hydrogen production rate (HPR) data can be yielded by fitting the kinetic data with empirical model such as modified
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Scenario I: Data Envelopment Analysis (DEA) Biohydrogen technology CRS and VRS, SE
Efficiency of batch tech.
Efficiency of continuous
of dark fermentation
tech. of dark fermentation
Scenario II: Statistical Analysis Tobit model Granger Causality Test Kolmogorov-Smirnov Normality Test
Soft-link Methodology: Efficiency improvement of scenario I to full efficiency (equals to unity)
Non-parametric Medium Test
Scenario III: Global Trade Analysis Project (GTAP) Model Effects of efficiency improvement of biohydrogen industry in six Asian regions with circular economy Fig. 1 e Soft-link integrated DEA-CGE model and scenario design.
Gompertz equation (eq. (1) in Ref. [42] in which the peak production rate of hydrogen is a fitting parameter. Although there exists ambiguity of definitions of the peak production rate reported in literature, this study adopts the HPR data instead of HY from Ref. [41] for DEA model estimation, and the major conclusions obtained in this study would remain unchanged since only the minor data corrections would occur in calculations. The input and output data with same units of batch (mass and onetime production) (Table 1) and continuous (Table 2) biohydrogen generation technology thus obtained. The production efficiency of DEA is standardized to unit which is easier to compare among different technologies in same basis.
Data for GTAP model China, India, Japan, South Korea, Malaysia and Taiwan are treated separately, while one hundred and thirty-four other
countries are aggregated as the “rest of world” in GTAP database. The fifty-seven sectors in the GTAP database are aggregated to thirty-one sectors, and the “Waste” sector and four bio-based sectors related to biohydrogen, bioplastic, biopharmaceuticals and genetically modified organisms are added. The final database includes seven regions and thirtysix sectors, along with model modification programming with CE mechanism. The output value of the waste sector is calculated as US$ 328.59 billion for China, US$ 79.63 billion for India, US$ 268.98 billion for Japan, US$ 52.53 billion for South Korea, US$ 13.02 billion for Malaysia, US$ 21.22 billion for Taiwan, US$ 1688.78 billion for the rest of the world, and US$2452.84 billion globally [4,21,43]. The production shares and sales shares of the waste and four bio-based sectors in the six regions are estimated and mapped to related GTAP sectors, for which supply and sales chains are constructed [21].
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 1 e The inputs and outputs of batch dark fermentation technology of biohydrogen production. Wastewater type
Sugar rich Cassava WW Rice mill WW Rice mill WW Rice mill WW CMS CMS CMS CMS CMS BWW BWW BWW BWW Sugar beet juice Distillery WW Dairy WW Dairy WW Complex dairy WW Organic WW Toxic/industrial Raw plastic Physico-chemical treated plastic industry Toilet aircraft Olive mill WW TWW CMS
Inoculum source
pH
Temperature ( C)
Substrate concentration (g COD/L)
Peak hydrogen production rate (HPR) (L/L/d)
Clostridium acetobutylicum ATCC824 Enterobacter aerogens Citrobacter ferundii Enterobacter aerogens RM08 Clostridium tyrobutyricum Clostridium pasteurianum Clostridium sporosphaeroides Clostridium pasteurianum þ Clostridium sporosphaeroides Clostridium tyrobutyricum þ Clostridium sporosphaeroides EMC-compost EMC þ E. Coli XL-1 BLUE EMC þ E. Cloacae EMC þ E. Coli XL1 blue Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Soil Anaerobic sludge Anaerobic sludge
7.0
36
17.5
0.60
6.5 6.5 6.5 7.0 7.0 7.0 7.0
33 33 33 35 35 35 35
15.8 15.8 16.1 50 50 50 40
1.72 1.61 1.71 1.48 3.69 0.48 2.97
7.0
35
60
0.62
5.5 5.5 5.5 6.5 5.5 5.5 5.5 5.5 5.5 5.0 5.75 5.5
37 37 37 37 37 37 37 37 37 30 36 36
10 10 25.8 5 4 34.8 15 15.3 56 10.5 3 3
1.81 1.25 3.04 1.75 3.2 2.88 1.45 2.31 0.89 0.32 0.28 0.1
Anaerobic sludge Anaerobic sludge
5.5 7.0 7.0 6.0
36 37 37 35
2 50 20 40
0.28 0.42 4.32 1.5
Anaerobic sludge
Source. Ref. [41] and modified by this study. Note. All notations come from Ref. [41]. BWW implies the “beverage wastewater”, CMS implies the “condensed soluble molasses”, WW implies the “Wastewater”.
A historical simulation of GTAP was carried out using historical economic data to generate databases from 2012 to 2017. To consider advances in biohydrogen dark fermentation technology, which is a rapid production technology, the impact shocks that are fed into GTAP model from the DEA model to determine when the lower production rate of biohydrogen technology to keep up with the benchmark technology (full production efficiency, implies efficiency equals to one) in the near future. Baseline forecasting from 2018 to 2022 was conducted by considering several exogenous shocks, including an increase in the global import prices of primary energy of 3.7415% per year, and by using population forecasts of the seven regions, which were obtained from the IMF website. The rates of technological improvement in the development of CRS and VRS scenarios for the biohydrogen sectors are set to 0.168575% and 0.191016% each year, respectively [18].
Scenario I. Evaluating DEA efficiency of CRS and VRS of forty-three dark fermentation (batch and continuous together) technologies of biohydrogen production. Scenario II. Performing the Tobit regression, Granger causality test, Kolmogorov-Smirnov normality distribution test, and Median tests to determine causality relationship of inputs and outputs of biohydrogen production. Scenario III. Calculating mean of technology efficiency generated by DEA from low efficient technologies (efficiency is less than 1) to benchmark technologies (efficiency equals to 1) then fed into dynamic GTAP model to reveal impacts on output value and supply price of biohydrogen in six Asian countries in CE.
Results and discussions Scenario I
Scenario design Scenarios specify the link between DEA and CGE. Several statistical tests are performed to completely describe the concepts built in this study. The designed scenarios are as follows.
Table 3 presents results concerning the CRS-DEA efficiencies of batch and continuous biohydrogen technology. The most efficient batch technology uses anaerobic sludge as an inoculum source and sugar beet juice as wastewater, providing a efficiency of 0.224, implying that a production efficiency
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 2 e The inputs and outputs of continuous dark fermentation technology of biohydrogen production. Inoculum
Indigenous microflora ADS AS AS ADS ADS AS Mixed culture ADS ADS ADS AS AS AS AS AS AS EMC
Waste water
Sweet sorghum extract Palm oil mill effluent Condensed molasses Condensed molasses Tofu processing WW Desugared molasses Sugarecane juice Soft-drink WW Cheese whey Tapioca WW Desugared molasses Tofu processing WW Sweet sorghum Dephenolized olive mill WW Molasses Sugary WW Beet sugar WW Beverage WW
Reactor mode
CSTR ASBR CSTR ICBR MBR UASB CSTR UAPBR UASB ABR UASB CSTR ASBR PBR CSTR CSTR CSTR ICBR
Substrate concentration (g COD/L)
pH
Temperature ( C)
HRT (h)
Rs
Rs
Rs
Rs
15.44 20.00 40.00 40.00 43.40 16.70 25.00 1.94 20.00 16.15 16.70 20.00 30.00 38.79 8.00 6.00 15.00 20.00
5.60 6.75 5.50 5.50 5.50 5.50 6.00 6.20 5.70 9.00 5.60 5.50 5.00 7.03 4.35 4.50 4.30 6.00
35.00 37.00 37.00 37.00 60.00 55.00 37.00 25.00 23.50 33.05 55.00 35.00 31.50 35.00 35.00 35.00 35.00 35.00
12.00 66.00 4.25 4.25 5.00 24.00 20.00 0.50 15.00 13.50 16.00 15.00 24.00 96.00 7.00 8.00 8.00 4.75
HPR (L/L/d)
2.93 6.70 14.04 7.60 19.86 4.50 2.29 9.60 8.64 0.83 5.60 1.73 3.20 7.00 7.47 3.45 10.80 55.40
Source. Ref. [41] and modified by this study. Note. All notations come from Ref. [41]. ADS implies “anaerobic digester sludge”; AS implies “anaerobic sewage sludge”; EMC implies “Enriched mixed culture”; CSTR implies “continuously stirred tank reactor; ASBR implies “anaerobic sequencing batch reactor”; ICBR implies “immobilized cell bioreactor”; MBR implies “membrane bioreactor”; UASB implies “upflow anaerobic blanket reactor”; UAPBR implies “upflow anaerobic packed bed reactor”; ABR implies “anaerobic baffled reactor”; PBR implies “packed bed reactor”; .
improvement potential of 77.6% (1 minus 0.224) will enable the anaerobic sludge-sugar beet juice batch technology to keep up with the full efficient production technology (efficiency is equals to 1 (100%)) which are mixed culture with soft-drink wastewater and EMC with beverage wastewater (continuous technology) in these forty-three dark fermentation technologies. The mean of efficiencies of the CRS of batch and continuous technologies are 0.045 and 0.251 respectively implying that the efficiency of continuous technology considerably exceeds 5.78 times (¼0.251/0.045) that of batch technology, and these two technologies have much room for future improvement (batch technology has improvement potential for 25 times, and continuous technology has improvement potential for 4 times, when their efficiency improve to 1). Results of efficiency of VRS-DEA exceed those of CRS because some DMUs did not reach their optimal economic production scale. The constraints on CRS are stricter than those on VRS and differences are caused by adequate production scale efficiency (SE). Also, for the most efficient batch technology (anaerobic sludge with sugar beet juice), SE is 0.569, implying that this technology improvement potential can reach an optimal production scale (SE equals to 1) by 43.1% (1 minus 0.569), as a result by increasing biohydrogen production output or resource allocation.
Scenario II Tables 4e7 presents the results of statistical tests of the CRSDEA efficiencies of batch and continuous biohydrogen technologies. Since fewer than 30 data points are available, their
probability distribution may not be normal so non-parametric statistical tests may be used to prevent statistical bias. The results of all followed tests will demonstrate the real impacts of the inputs on biohydrogen generation. First, the range of efficiencies that are obtained using DEA is standardized to zero to one, which enable the Tobit regression model to be used (independent variable of Tobit model is accumulated probability also between zero and unity) to determine the causal relationships between production efficiency (independent variable) and pH, temperature, substrate concentration and hydrogen production rate (HPR) (dependent variables). The results indicate that the substrate concentration (p-value < 0.05) and HPR (p-value < 0.01) significantly affect production efficiency statistically. HPR is hydrogen output, which directly affects production efficiency. Substrate concentration is also an important factor that influences the biohydrogen production efficiency (in Table 4 for CRS). Second, Granger causality test is conducted to determine pairwise relationships between inputs and outputs of biohydrogen production. Most of the causality tests do not enable the null hypothesis (H0) to be rejected (Table 5), so the causal relationships between the inputs and outputs of biohydrogen generation technology cannot be identified. The Kolmogorov-Smirnov normality test is carried out to test the probability distribution is normal or not. The results in Table 6 reveal that the inputs and DEA efficiency of the biohydrogen are not normally distributed so a parametric method such as ANOVA analysis cannot be used to test the mean impacts of various inputs and efficiency between two biohydrogen production technologies.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 3 e Biohydrogen production efficiency of batch and continuous technology. Batch biohydrogen production Inoculum source
Wastewater type
CRS
VRS
SE
Sugar rich Cassava WW Rice mill WW Rice mill WW Rice mill WW CMS CMS CMS CMS
0.012 0.038 0.036 0.038 0.027 0.067 0.009 0.054
0.012 0.038 0.036 0.038 0.027 0.067 0.009 0.054
0.991 0.999 0.999 0.999 1.000 1.000 1.000 1.000
CMS
0.011
0.011
1.000
BWW BWW BWW BWW Sugar beet juice Distillery WW Dairy WW Dairy WW Complex dairy WW Organic WW Toxic/industrial Raw plastic Physico-chemical treated plastic industry Toilet aircraft Olive mill WW TWW CMS
0.061 0.042 0.060 0.099 0.224 0.057 0.034 0.054 0.018 0.010 0.023 0.008
0.075 0.052 0.072 0.101 0.393 0.068 0.039 0.061 0.021 1.000 0.035 0.022
0.813 0.813 0.833 0.986 0.569 0.833 0.881 0.884 0.833 0.010 0.661 0.382
0.030 0.007 0.077 0.027 0.045
1.000 0.008 0.078 0.027 0.134
0.030 0.946 0.993 1.000 0.818
Inoculum Source
Wastewater type
CRS
VRS
SE
Indigenous microflora ADS AS AS ADS ADS AS Mixed culture ADS ADS ADS AS AS AS AS AS AS EMC Average
Sweet sorghum extract Palm oil mill effluent Condensed molasses Condensed molasses Tofu processing WW Desugared molasses Sugarcane juice Soft-drink WW Cheese whey Tapioca WW Desugared molasses Tofu processing WW Sweet sorghum Dephenolized olive mill WW Molasses Sugary WW Beet sugar WW Beverage WW
0.067 0.120 0.276 0.150 0.391 0.097 0.041 1.000 0.232 0.018 0.120 0.034 0.069 0.126 0.316 0.185 0.272 1.000 0.251
0.074 0.121 0.332 0.180 0.470 0.110 0.041 1.000 1.000 0.018 0.131 0.041 0.185 0.126 1.000 1.000 1.000 1.000 0.435
0.906 0.993 0.833 0.833 0.833 0.881 1.000 1.000 0.232 0.999 0.915 0.833 0.375 1.000 0.316 0.185 0.272 1.000 0.745
Clostridium acetobutylicum ATCC824 Enterobacter aerogens Citrobacter ferundii Enterobacter aerogens RM08 Clostridium tyrobutyricum Clostridium pasteurianum Clostridium sporosphaeroides Clostridium pasteurianum þ Clostridium sporosphaeroides Clostridium tyrobutyricum þ Clostridium sporosphaeroides EMC-compost EMC þ E. Coli XL-1 BLUE EMC þ E. Cloacae EMC þ E. Coli XL1 blue Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Soil Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Anaerobic sludge Average Continuous biohydrogen production
Note. All abbreviations are as in notes in Tables 1 and 2. TWW implies “textile wastewater”.
Based on the results in Table 7, non-parametric tests are performed to determine whether the efficiencies and inputs of batch and continuous production technology are similar or significantly different. The results demonstrate that the median efficiencies of batch and continuous biohydrogen production technology are significantly different with each other, but other inputs have almost the same medians, implying that the efficiency and HPR of
biohydrogen generation are determined by the production method (batch or continuous), rather than not by inputs (which are similar). According to the test results for scenario II, substrate concentration is the most important biohydrogen generation input; pH influences the efficiency of batch technology; and the efficiency and HPR of continuous production are significantly different (greater) than those of batch technology.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 4 e Tobit model’s causality relationships estimation of CRS efficiency among pH, temperature, substrate concentration and HPR. Batch production technology Variable Constant pH Temperature Substrate concentration HPR
Coefficient 0.096 0.003 0.004 0.001 0.024
Standard Error 0.141 0.010 0.003 0.000 0.005
P-value 0.497 0.768 0.256 0.037** 0.000***
Continuous production technology Variable
Coefficient
Standard Error
P-value
Constant pH Temperature Substrate concentration HPR
0.421 0.004 0.005 0.007 0.019
0.269 0.036 0.004 0.003 0.003
0.117 0.901 0.283 0.034** 0.000***
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
Scenario III The efficiency improvements to benchmark technology efficiency of the biohydrogen sector in six Asian countries, obtained by DEA, are fed into the GTAP-CGE model for batch and continuous biohydrogen generation technology, as shown in Table 8. Baseline results, which imply the driven power of development of biohydrogen industry without policy support
Table 6 e KeS Normality tests for CRS/VRS and inputs in DEA simulation. Null Hypothesis (H0)
P-value
Decision
CRS’s distribution is normal distribution VRS’s distribution is normal distribution pH’s distribution is normal distribution Temperature’s distribution is normal distribution Substrate’s distribution is normal distribution HPR’s distribution is normal distribution
0.000*** 0.000*** 0.001*** 0.000***
Reject Reject Reject Reject
0.000***
Reject H0
0.000***
Reject H0
H0 H0 H0 H0
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
and external technology improvement, reveals that the level of output quantity of the biohydrogen industry are high in India (around 8%) and China (around 6%), followed by Korea (around 4.66%e4.82%), Malaysia (around 4%), Taiwan (under 3%) and Japan (around 1%). Level results of batch and continuous technology are slightly higher than baseline but is less than 2 decimal places (make results indifferent in upper part of Table 8). The real effects of increasing production efficiency for batch and continuous technology from baseline efficiency on output quantity of biohydrogen should be counted by results of batch minus results of baseline, and results of continuous minus results of baseline (in bottom of Table 8). The real effects of two technologies are slightly increasing in all countries, Japan (0.016% and 0.015%) and India (0.01% and 0.008%) are top two countries. Japan and India are the two countries that most benefit from improvements in the efficiencies of batch and
Table 5 e Granger causality test of CRS-DEA efficiency among HPR, pH, temperature and substrate concentration. Batch production technology Null Hypothesis (H0) HPR does not Granger Cause CRS CRS does not Granger Cause HPR pH does not Granger Cause CRS CRS does not Granger Cause PH T does not Granger Cause CRS CRS does not Granger Cause T SUB does not Granger Cause CRS CRS does not Granger Cause SUB VRS does not Granger Cause CRS CRS does not Granger Cause VRS pH does not Granger Cause HPR HPR does not Granger Cause PH T does not Granger Cause HPR HPR does not Granger Cause T SUB does not Granger Cause HPR HPR does not Granger Cause SUB T does not Granger Cause PH pH does not Granger Cause T SUB does not Granger Cause PH pH does not Granger Cause SUB SUB does not Granger Cause T T does not Granger Cause SUB
Continuous production technology F-Statistic
P-value
Null Hypothesis (H0)
F-Statistic
P-value
2.55 0.57 2.90 0.59 0.45 0.19 0.01 1.56 0.31 0.31 2.16 1.46 0.51 0.11 0.73 1.95 1.29 0.35 0.33 1.93 1.46 0.08*
0.11 0.58 0.08* 0.57 0.64 0.83 0.99 0.24 0.73 0.73 0.14 0.26 0.61 0.89 0.49 0.17 0.30 0.71 0.72 0.17 0.26 0.92
HPR does not Granger Cause CRS CRS does not Granger Cause HPR pH does not Granger Cause CRS CRS does not Granger Cause PH T does not Granger Cause CRS CRS does not Granger Cause T SUB does not Granger Cause CRS CRS does not Granger Cause SUB VRS does not Granger Cause CRS CRS does not Granger Cause VRS pH does not Granger Cause HPR HPR does not Granger Cause PH T does not Granger Cause HPR HPR does not Granger Cause T SUB does not Granger Cause HPR HPR does not Granger Cause SUB T does not Granger Cause PH pH does not Granger Cause T SUB does not Granger Cause PH pH does not Granger Cause SUB SUB does not Granger Cause T T does not Granger Cause SUB
0.13 0.04 1.79 4.66 0.76 0.51 0.63 0.06 1.11 2.45 2.67 0.10 0.23 3.43 0.59 0.13 0.54 1.54 1.26 0.07 2.71 0.10
0.88 0.96 0.21 0.03** 0.49 0.61 0.55 0.94 0.36 0.13 0.11 0.91 0.80 0.07* 0.57 0.88 0.60 0.26 0.32 0.94 0.11 0.91
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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Table 7 e Median test of efficiency of batch and continuous biohydrogen production technology. Null Hypothesis (H0)
P-value
CRS’s median between batch and continuous tech. are same VRS’s median between batch and continuous tech. are same pH’s median between batch and continuous tech. are same Temperature’s median between batch and continuous tech. are same Substrate’s median between batch and continuous tech. are same HPR’s median between batch and continuous tech. are same
0.000*** Reject H0
Decision
0.000*** Reject H0 0.425
Do not reject H0
0.983
Do not reject H0
0.661
Do not reject H0
0.000*** Reject H0
Note. The “***”, “**” and “*” imply the p-values are statistical significantly reject null hypothesis (H0) at 1%, 5% and 10% significance level.
continuous biohydrogen production technology. An annual decrease in the supply price of biohydrogen also favors the development of biohydrogen by reducing the costs of those who use biohydrogen. The efficiency of batch technology is lower than that of continuous technology, and the former requires more efficiency improvement to reach optimal production conditions, which will generate more benefit for the biohydrogen industry and upstream and downstream supply chains.
Conclusion In this work, a new soft-link methodology that combines DEA and CGE models is developed to incorporate both production efficiency and technological improvements and their benefits on biohydrogen production in six Asian CE regions. Input and output data for batch and continuous biohydrogen production
Table 8 e Efficiency improvement of DEA induces development of biohydrogen sector by dynamic GTAP model in six Asian CE countries. Output quantity of biohydrogen
Supply price of biohydrogen
Baseline (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.71 0.39 4.78 3.70 3.00
6.57 8.52 0.50 4.70 4.00 2.89
6.17 8.29 0.71 4.66 4.24 2.76
5.73 8.02 0.99 4.63 4.44 2.63
5.52 7.94 1.37 4.82 4.73 2.81
Baseline (level) China India Japan Korea Malaysia Taiwan
2018
2019
2020
2021
2022
0.69 1.49 1.58 1.37 0.04 1.33
0.63 1.29 1.59 1.52 0.13 1.49
0.57 1.06 1.55 1.66 0.19 1.64
0.51 0.81 1.48 1.78 0.25 1.79
0.45 0.52 1.53 1.95 0.27 1.97
Batch (level)
2018
2019
2020
2021
2022
Batch (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.72 0.41 4.79 3.71 3.01
6.57 8.53 0.51 4.71 4.01 2.90
6.17 8.30 0.73 4.67 4.25 2.77
5.73 8.03 1.01 4.64 4.45 2.64
5.52 7.95 1.39 4.83 4.73 2.82
China India Japan Korea Malaysia Taiwan
0.88 1.68 1.39 1.17 0.24 1.14
0.82 1.48 1.40 1.33 0.32 1.30
0.76 1.25 1.36 1.47 0.39 1.45
0.70 1.00 1.29 1.59 0.44 1.60
0.64 0.71 1.33 1.76 0.46 1.78
Continuous (level)
2018
2019
2020
2021
2022
Continuous (level)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
6.93 8.72 0.41 4.78 3.71 3.01
6.57 8.53 0.51 4.71 4.01 2.9
6.17 8.3 0.73 4.67 4.25 2.77
5.73 8.03 1.01 4.64 4.44 2.64
5.52 7.95 1.39 4.83 4.73 2.82
China India Japan Korea Malaysia Taiwan
0.86 1.66 1.41 1.20 0.21 1.16
0.80 1.46 1.42 1.35 0.30 1.32
0.74 1.22 1.38 1.49 0.36 1.47
0.68 0.98 1.31 1.62 0.42 1.62
0.61 0.69 1.36 1.78 0.44 1.80
Batch minus baseline (difference)
2018
2019
2020
2021
2022
Batch minus baseline (difference)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
0.003 0.011 0.016 0.009 0.007 0.010
0.002 0.010 0.016 0.009 0.007 0.010
0.002 0.009 0.016 0.008 0.007 0.010
0.002 0.008 0.016 0.008 0.007 0.009
0.001 0.006 0.016 0.007 0.007 0.009
China India Japan Korea Malaysia Taiwan
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.192
0.192 0.191 0.192 0.192 0.192 0.191
0.191 0.190 0.192 0.191 0.192 0.191
Conti. minus baseline (difference)
2018
2019
2020
2021
2022
Conti. minus baseline (difference)
2018
2019
2020
2021
2022
China India Japan Korea Malaysia Taiwan
0.003 0.010 0.014 0.008 0.006 0.009
0.002 0.009 0.015 0.008 0.006 0.009
0.002 0.008 0.015 0.007 0.006 0.008
0.002 0.007 0.015 0.007 0.006 0.008
0.001 0.006 0.015 0.007 0.006 0.008
China India Japan Korea Malaysia Taiwan
0.169 0.169 0.169 0.169 0.170 0.169
0.169 0.169 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
0.169 0.168 0.170 0.169 0.170 0.169
Please cite this article as: Lee D-H, Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE), International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.08.250
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international journal of hydrogen energy xxx (xxxx) xxx
technologies are collected and modified, and efficiencies of forty-three technologies are estimated. Results found that the improvement potentials of two technologies leave much to be desired. The production efficiency of continuous technology is higher than batch technology which implies if firms adopt continuous technology to produce biohydrogen will obtain higher output quantity or lower cost. The supply price of biohydrogen will decrease (around 0.168% to 0.192%) when the batch and continuous technologies improve from baseline efficiency to full production efficiency. Biomass substrate concentration is found to be the most important input of biohydrogen generation, pH influences the efficiency of batch technology, and the efficiency and HPR of continuous production are significantly greater than those of batch technology. India and China are top two countries which generate highest output growth of biohydrogen in baseline scenario, but Japan and India are the two countries that most benefit from improvements in the efficiencies of batch and continuous biohydrogen production technology. Results reveals that the basic driven power in current production efficiency of biohydrogen is high in most of Asian countries, but the technology improvement generates slightly positive impact of increase biohydrogen output quantity because of the competition of other hydrogen sources and renewable energies. Efficiency improvements also favor the development of the biohydrogen industry and its supply chain, the development of CE and the bioeconomy. Results verify that the DEA-CGE model that is developed herein is workable. The models and results in this work provide guidelines and references for decision-maker in industry and government who are responsible for reforming future energy policy.
references
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