Simulation Analysis of the Construction and Introduction of Biomass Energy Utilization System in Northern Rural
Area in China: A case study of Changchun City, Jilin Province
Tsukuba University

SONG Junnian*

Tsukuba University

YANG Wei

Tsukuba University

HIGANO Yoshiro

Tsukuba University

MIZUNOYA Takeshi

1. Introduction
Jilin Province, located in the northeast of China, is important industrial base and commodity grain base of China.
Accelerating industrialization, low primary energy reserve and unreasonable energy utilization patterns have resulted in the
traditionally constant dependence on imported energy and a energy consumption structure dominated by fossil energy. Low
energy self-sufficiency rate (around 55%) and unreasonable energy consumption structure are urgent problems for Jilin
Province to solve. As a traditional agroforestry province, wide arable land, high yield of grain, scaled breeding andagroforestry industries are providing material basis for biomass development and utilization in Jilin Province. In view of theabove, Changchun City in Jilin Province has been selected as the typical target area of this study.
In this paper, BEIs with agricultural residues (AR), forestry residues (FR), livestock manure (LM) and municipal solid
waste (MSW) identified as the extensive and abundant feedstocks for energy conversion for Jilin Province case will be
introduced into socioeconomic activities along with an adoption of carbon tax-subsidization policy for bioenergy industrial
promotion. A dynamic optimization simulation model will be developed to comprehensively assess the induced economic,
energetic and environmental impacts with the maximum Gross Regional Production (GPR) as the objective function subject
to restrictions of GHGs emission. The extent to which bioenergy industrial development, energy crisis mitigation and GHGs
intensity reduction trend can be achieved will be simulated within a 16-year time horizon. LINGO software developed by
LINDO System Company will be adopted to solve the mathematical linear programming problem. It’s expected to reach adecision regarding the optimal solution to the simultaneous pursuit of energy utilization, environmental preservation andeconomic development in study area and achieve the governmental planning goals.
2. Methodology
2.1 Outline of DOSM
The schemed DOSM consists of several major economic entities as usual industries (UIs), conventional energy industries(CEIs), final consumption sectors (government and private sectors) and the newly introduced bioenergy industries (BEIs) as
shown in Fig. 1.. UIs and CEIs consume resources from natural environmental system and after production activities
provide interrelated sectors and final consumption sectors with commodities, services and energy (or energy products). CEIs
along with BEIs and imported energy meet the energy demand of UIs and final consumption. The organic wastes generated
from UIs and final consumption are collected and reused by BEIs as biomass feedstock supply sources. GHGs are emitted
from each production and consumption activity that consumes fossil energy into environmental system. In addition to direct
and indirect taxes as governmental revenue, a carbon tax is imposed on UIs, CEIs and private consumption as acountermeasure fund to subsidize the BEIs dedicatedly for stimulating the industrial development.

1Government andprivate savings

Other regions
Investment

Import and export

Final consumption sectors

Industries
PrivateincomeConventional

Private sectors

energy industries
Usual industries

Direct tax
Indirecttax

Biomass energy

Government

industries

Commodities and services

Money

Biomass organic wastes

EnergyCarbon tax and subsidies

Fig. 1. Framework of the dynamic optimization simulation model
2.2 Bioenergy flow
The complete bioenergy flow calls for procurement, supply, energy conversion and consumption four combinatorial
factors, each of which could result in economic impacts to related industries after BEIs are introduced into current
socioeconomic activities. A schematic bioenergy flow is shown in Fig. 2.. Industries of farming, forestry, breeding andservices and domestic consumption are providing various organic bioresources for energy production as biomass feedstock
sources. According to corresponding bioresources, five bioenergy conversion technologies (bioenergy projects) that havealready been tentatively initiated in Jilin Province and have the potential for large scaled promotion have been selected andthe related parameters are shown in Table 1. Based on the five selected bioenergy conversion technologies, five new BEIs as
Biomass combustion power generation (BEI-T1), Biomass gasification power generation (BEI-T2), Biomass solid molding
fuel (BEI-T3), Large and medium scaled breeding farm biogas power generation (BEI-T4) and MSW incineration power
generation can be developed (BEI-T5).

2

Farming
industry

Forestryindustry

Breeding
industry

Services &domestic
consumption

Agricultural
resources

Forestry
residues

Livestock
manureMunicipal
solid waste

Adhering tobreeding farm

City oriented
collection
pattern

Biomass
procurement

Resource island collection pattern
Biomass
supplyPretreatment and transportation

Bioenergy
conversion

Combustion
power
generation

Bioenergy
consumption

Gasification
power
generation

Electricity

Biomass resourcesSolidmoldingfuels

Breeding
farm biogas
power
generation

Fuels

EnergyMSW
incineration
power
generation

Heat

Bioenergy utilization flow

Fig. 2. Schematic bioenergy flow
2.3 Modeling Framework
The I-O table of Jilin Province (2007) with 142 industrial categories are adopted to make I-O analysis. The UIs have been
aggregated into 13 major sectors, and the CEIs have been sorted into 5 sectors. Five BEIs are additionally introduced as
aforementioned.
The variables are divided into endogenous (en) and exogenous (ex) ones in the simulation model. The exogenous ones arederived according to realistic data and the exogenous ones will be determined by the model structure. The objective function
is maximum GRP subject to the balances of material flow, value flow and energy flow and distinct levels of GHGs emission
constraints.
(1) Balance of material flow
The commodities or services that supplied by usual industries are no less than those demanded by four sources asintermediate industrial input, government and private consumption, investment and net export. t stands for the t-th year ofsimulation term.
3

X 1(t)   A1i X i (t)  C 1(t)  G1  I 1(t)  R1(t)

(1)

i 1

(2) Balance of energy flow
Based on the premise of the identity of energy products produced by CEIs and BEIs, energy from both industries could
collectively meet the energy demand of intermediate industrial energy input, governmental and private energy consumption,
investment of energy industries and net energy export. The CEIs and BEIs are integrally regarded as a complete energy
industries (EIs) thus the output of both industries are added up.
3

X e(t)   Aei X i (t)  Ce(t)  Ge  I e(t)  Re(t)

(2)

i 1

(3) Balance of value flow
The income of an industry is no larger than its expense comprised of those for intermediate input, business surplus andemployer income, depreciation and indirect taxes. In addition to the general expenses of UIs and CEIs, a carbon tax is
proposed to be imposed according to the GHGs emission amount. And the levied carbon tax will be dedicatedly utilized as a3

countermeasure fund subsidizing the BEIs, which functions as an additional income of BEIs.

~
~
~
~
~
~
~
P1(t)X 1(t)  P1(t)A11 X 1(t)  Pe(t)Ae1 X 1(t)  Y1(t)  δ1 K 1(t)  τ 1 X 1(t)  τ c w
1GHG X 1(t)

(3)

~
~
~
~
~
~
~
Pe(t)X 2(t)  P1(t)A12 X 2(t)  Pe(t)Ae2 X 2(t)  Y2(t)  δ2 K 2(t)  τ 2 X 2(t)  τ c w
2GHG X 2(t)

(4)

~
~
~
~
~
Pe(t)X 3(t)  Sub(t)  P1(t)A13 X 3(t)  Pe(t)Ae3 X 3(t)  Y3(t)  δ3 K 3(t)  τ 3 X 3(t)(5)(4) Flow conditions of BEIs
Five bioenergy technologies call for corresponding biomass feedstocks, which determine the total number of eachbioenergy project along with the feedstock supply amount of unit bioenergy project. The linearity is assumed between thecollectable and utilizable amount of each bioresource and output of its correlated industry. Total output of each BEI iscomprised of the output of all bioenergy projects of this industry.

N Ti (t) ARTi (t)  FRTi (t)(i=1,2,3)
AFRSTi(6)

NT 4(t) 

CM T 4(t) HM T 4(t)
CM ST 4
HM ST 4

(7)

SWT 5(t)SWST 5(8)

NT 5 ( t ) n

QAR(t)   qc j X 1(t)

(9)

j

n

QFR(t)   qf j X 1(t)(10)

QLM (t)  qdc X 1(t)  qdh X 1(t)(11)

QSW (t)  qs1 X 1(t)  qsc  C1(t)

(12)

j

5

X 3   zTi N Ti (t)

(13)

i 1

(5) GHGs emission flow
GHGs are considered to be emitted by production and consumption activities that consume fossil energy. Amount of all
GHGs are converted to CO2 equivalent for unified calculation. GHGs emission amount is assumed to be related to output ofindustries and private energy consumption linearly.

WGHG (t)  w1GHG X 1(t)  w2GHG X 2(t)  w3GHG X 3(t)  wCGHG Ce(t)(14)(6) Private income and expenditure
Private income encompasses business surplus and employer income of each industry determined by the income rateembedded in the I-O table. Total private income of all industries contains direct tax, and disposable income which consistsof private saving and private consumption. Share of private consumption of UIs and EIs is set to be constant according to
I-O table. Particularly, the levied carbon tax for energy consumption is included as part of private consumption.

~
Yi (t)  yi X i (t) (i=1,2,3)

(15)

4

3

Yd (t)  1   d   tYi (t)(16)

P1(t)C1(t)  1   Yd (t)α1(17)Pe(t)Ce(t)   c wCGHG Ce  1   Yd (t) e(18)

S p(t)  Yd (t)

(19)

i 1

(7) Government revenue and expenditure
Government revenue comprised of direct tax levied on private income, indirect taxes levied on all industries and newly
introduced carbon tax levied on UIs, CEIs and private consumption is equal to sum of government consumption,
government saving and subsidies for BEIs.
3

3

2

i 1

i 1

i 1

 τ i X i(t)   d   tYi(t)  τ c  wiGHG X i(t)  τ c wCGHG Ce(t)  G 1  Ge  S g(t)   t Sub(t)(20)(8) Investment and saving balance
All the private and government savings are used as investment and net export for industries for facilitating maximized
industrial development.
3 I (t)  δ K (t)  R (t)  R (t)  S (t)  S (t)
i

i

i

1

e

p

(21)

g

i 1

(9) Capital and production
Technologies adopted by each industry are assumed to be constant during the simulation time horizon thus the output ofeach industry follows a linear production function related to capital stock through fixed coefficient.

γ~i X i (t)  K i (t) (i=1,2,3)(22)(10) Dynamic equation of capital stock
The capital accumulation relies on the depreciation of capital stock and investment of the previous year.





~ ~
K i (t  1)    δi K i (t)  I i (t) (i=1,2,3)(23)(11) GHGs emission constraint
GHGs emission amount in term t+1 is set not to exceed a certain percentage of that in term t. The growth rate of GHGs
emission amount conforms to a linear function as the follows.

WGHG (t  1)  WGHG (t) a(t  1)  b (1≤t≤15)
WGHG (t)(24)(12) Objective function
The optimal level of the entire socioeconomic activities is obtained by solving the a maximization problem of GRP
subject to all balance relationships and GHGs emission constraint. Simulation of 16 terms from 2010 to 2025 will be
operated.
3

GRP(t)   ν i X i (t) (i=1,2,3)

(25)

i 1

t 1

16
 1 
 GRP(t) (ρ=0.05)
MAX  t 1  1   (26)

2.4 Scenarios setting
Achievement of GRP growth inevitably leads to the increase of GHGs emission. With GRP growth as premise ofmodeling optimization, the simulation will be operated in several scenarios set based on GHGs emission constraints, newly
introduced BEIs, and policy of carbon tax-subsidization. Five levels of GHGs emission growth rate have been set (see Table51) to accomplish the reduction of total GHGs emission amount and maximization of GRP within the whole time horizon.
The growth rate of GHGs emission for each level is set to conform linear function with distinct slopes and to be lower than
GRP growth rate in the second term (15.00%<16.28%) and more and more strictly constrained (14%-11%). In the final term
GHGs emission growth rate is set to be as low as 4%1 in order to detect a proper average growth rate of GHGs emissionthat could be compared with average GRP growth rate more reasonably and not to incur a excessively low GRP growth rate.
Details of each scenario are illustrated in Table 2.
Table 1. Setting of GHGs emission growth rate
Restrictions

R1

R2

R3

R4

R5

Range of GHGs emission growth rate (2008-2025)

15.00%→4%

14.00%→4%

13.00%→4%

12.00%→4%

11.00%→4%a-0.006468-0.005885-0.005295-0.004705-0.004115b0.14350.13420.12470.11530.1058Average GHGs emission growth rate

9.50%

9.00%

8.50%

8.00%

7.50%

Table 2. Setting of modeling simulation scenarios
GHGs emission constraint
Scenarios
R1
Scenario 0
Scenario 1
Scenario 2

R2

R3

×
×
√

R4

BEIs

Carbon tax and subsidization

×
√
√

×
√
√

R5

Scenario 0 (S0) is set as “business as usual” to predict the actual trend of economic development, energy production andconsumption, and GHGs emission. BEIs along with carbon tax-subsidization is set to be introduced in Scenario 1 (S1) toclarify the effects induced by introduction of BEIs on the whole energy consumption structure and environmental impact
mitigation. Base on Scenario 1, GHGs emission constraints are set additionally in Scenario 2 (S2) to identify the optimallevel of GHGs constraint and carbon tax rate. Five sub-scenarios S2-R1to S2-R5 are set in S2 according to distinct levels of
GHGs emission constraints.
4. Summaries
A dynamic optimization simulation model has been developed based on I-O analysis to dynamically assess the economic,
energetic and environmental impacts induced by the introduction of bioenergy industries, carbon tax-subsidization policy
and distinct GHGs emission constraints from several scenarios preset taking Changchun City in Jilin Province as target
region.
According to the dynamic simulated results of economy, energy and environment of 16 terms, an optimal scenario has
been determined in terms of higher GRP growth rate with modeling constraints and governmental planning targets, forwhich 8.41% average GRP growth rate can be achieved with 30.89% reduction of accumulative GHGs emission comparedwith when no bioenergy industries and GHGs emission constraints introduced. By the final term 2025, primary energy
self-sufficiency rate can be increased to 77.9% and primary energy consumption structure can be adjusted resulting in
reduction of coal consumption ratio, and increase of new and renewable energy consumption ratio with bioenergy
accounting for 8.2%. Proper carbon tax rate has been explored annually to quantify amount of carbon tax as dedicatedsubsidization for promoting rapid development of bioenergy industries. Industries of combustion power generation and solid
molding fuels have better prospects contributing to more than 80% of accumulative bioenergy production. A complete
bioenergy flow from feedstock acquisition to energy consumption has been presented quantitatively.
It can be proved that introduction of bioenergy industries combined with carbon tax-subsidization policy and GHGs
constraints can facilitate the simultaneous pursuit of GRP increase, adjustment of energy consumption structure andenvironmental impacts mitigation effectively and indicate the GHGs mitigation direction for major GHG-contribution
1

Actual simulation work proves that with GHGs emission growth rate lower than 4%, no global feasible solution could be obtained because of
extremely strict constraint.
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industries quantitatively. The dynamic optimization simulation model performances well at dynamically predicting the
economic and environmental effects induced by introduction of new industries and new policies and exploring an optimallevel of relationships among all socioeconomic activities. The methodology adopted and results presented are expected toprovide regional decision makers with reference for economic and energy policy proposal.
Glossary
X1(t): output of UIs, (vector, en);
A1i: input coefficients from UIs to industry i, (matrix, ex);
C1(t): private consumption of UIs, (vector, en);—

G1: government consumption of UIs, (vector, ex);
I1(t): investment of UIs, (vector, en)
R1(t): net export of UIs, (vector, en);Xe(t): output sum of CEIs and BEIs, (scalar, en);
Aei: input coefficients from EIs to industry i, (vector, ex);Ce(t): private consumption of EIs, (scalar, en);—

Ge: government consumption of EIs, (scalar, ex);Ie(t): investment of EIs, (scalar, en);Re(t): net export of EIs, (scalar, en);
P1(t): price adjustment rate of UIs, (vector, en);Pe(t): price adjustment rate of EIs, (scalar, en);Yi(t): private income (business surplus and employer income) of industry i, (vector, en);δi: depreciation rate of industry i, (vector, ex);Ki(t): capital stock of industry i, (vector, en);
τi: indirect tax rate of industry i, (vector, ex);
τc: carbon tax rate, (scalar, ex);
 : unit row vector;
wiGHG: GHGs emission coefficient of industry i, (vector, ex);
Sub(t): subsidy to BEIs, (vector, en);NTi(t): the number of bioenergy project Ti, (scalar, en);ARTi(t): the amount of AR utilized by BEI-Ti, (scalar, en);
FRTi(t): the amount of FR utilized by BEI-Ti, (scalar, en);
AFRSTi: the amount of AR or FR utilized by unit bioenergy projects Ti, (scalar, ex);
CMT4(t): the amount of cattle manure utilized by BEI-T4, (scalar, en);
HMT4(t): the amount of hog manure utilized by BEI-T4, (scalar, en);
CMST4: the amount of cattle manure utilized by unit bioenergy project T4, (scalar, ex);
HMST4: the amount of hog manure utilized by unit bioenergy project T4, (scalar, ex);
SWT5(t): the amount of MSW utilized by BEI-T5, (scalar, en);
SWST5: the amount of MSW utilized by unit bioenergy project T5, (scalar, ex);
QAR(t): total amount of AR that can be collected and utilized, (scalar, en);
QFR(t): total amount of FR that can be collected and utilized, (scalar, en);
QLM(t): total amount of LM that can be collected and utilized, (scalar, en);QSW(t): total amount of MSW that can be collected and utilized, (scalar, en);
Qcj: generation coefficient of AR of crop j from UIs, (row vector, ex);Qfj: generation coefficient of FR of forestry j from UIs, (row vector, ex);qdc: generation coefficient of cattle manure from UIs, (row vector, ex);qdh: generation coefficient of hog manure from UIs, (row vector, ex);qs1: generation coefficient of MSW from UIs, (row vector, ex);7

qsc: generation coefficient of MSW from private consumption of UIs, (scalar, ex);zTi: unit output of bioenergy project Ti, (scalar, ex);
WGHG: total GHGs emission amount, (scalar, en);wCGHG: GHGs emission coefficient of private energy consumption, (scalar, ex);
yi: income rate of industry i, (vector, ex);Yd(t): disposable income, (scalar, en);τd: direct tax rate, (scalar, ex);β: private saving rate, (scalar, ex);
αi: share of industry i’s consumption in total private consumption (i=1,e), (scalar, ex);
Sp(t): private saving, (scalar, en);Sg(t): government saving, (scalar, en);
γi: capital production coefficient industry i, (vector, ex);
νi: value added rate of industry i, (vector, ex);
ρ: discount rate, (scalar, ex).

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