Comprehensive analysis of the potential of introducing renewableenergy to reduce GHG and SO2 emissions in Chonqing city, China
Qian Zhou*, Takeshi Mizunoya, Helmut Yabar, Yoshiro Higano
Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan

1. Introduction
The access to a stable energy supply is a very important condition to achieve sustainable
development. China has largely relied on the utilization of fossil fuels, especially coal, as a
primary energy source to meet increasing demand for economic development. This situation
has led to an inappropriate energy consumption structure. Furthermore, environmentaldegradation including acid rain associated with this unsustainable energy structure isbecoming more prominent, with the rapid economic development. Renewable energy, aneffective tool in reducing SO2 and GHG emissions and in providing energy supply, hasattracted increasing attention worldwide as clean energy resource (Dincer, 2000; Menegaki,2008; Zheng, Zhang, Yu, & Lin, 2011). Chongqing is a typical area in this respect; almost allof its energy comes from fossil fuels particularly coal. The proportion of coal to the totalenergy utilization was 70 % in 2010 (Chongqing Municipal Bureau of Statistics, 2011). The
reliance on coal for energy supply has caused environmental problems including acid rain andair pollution in addition to the rapid increase in GHG emissions. It is significant for
Chongqing to introduce renewable energy technologies to improve the local energy structure
while achieving the SO2 and GHG emission targets set by the Chinese Central Government.
Chongqing is located in Southwest China, where the distribution rate of economically
exploitable hydro resources in China is 58.9 % (Huang & Yang, 2009). This means
hydropower will be an important potential renewable energy contributing to energy structuretransformation. Additionally in the “12th Five Year Plan”, the Chongqing government plans tointroduce wind power technologies to promote the wind power industry. In this process,evaluation of candidate renewable technologies is a significant step for government
policy-makers to decide budget allocation, mitigation plans, energy and economicdevelopment. Consequently, the specific objectives of this study are to comprehensively
analyze the impact of small-scale hydropower and wind power technologies to explore
renewable energy potential in Chongqing and the feasibility of introducing these technologies.
We chose the “Chongqing Siyangping 49.3 MW Wind Power Project” and the “9.6 MW
Xiaohe Small Hydropower Project, China”, registered CDM projects as reference for the1potential of renewable energy introduction. “Renewable technologies” refers to these twokinds of technology projects in this paper.
Recently, many studies employing Input-Output models have been undertaken tocomprehensively evaluate policies which act as a motivation to make optimal balancebetween economic development and environmental protection. Mizunoya and Higano (1999)
simulated an optimal tax-subsidy to reduce air pollutant (CO2, SO2, NOx) emissions in Japan.
Li et al. (2012) analyzed the impact of a recycling tax on metal recycling and GHG emission.
Sakurai et al. (2003) proposed an environmental tax rate to maximize GDP under restrictionof GHG emission based on an I/O model. Li and Higano (2004) simulated the best
environmental tax rate for policies to introduce renewable energies for emission reduction inChina. Wang et al. (2012) investigated an environmental tax to develop biomass power
technologies for GHG mitigation.
However, limited research has been undertaken to comprehensively evaluate the benefits ofrenewable energy technologies via a simulation model and later identify the feasibility ofintroducing them. In this study, we propose an integrated environmental management policy
using a computer simulation that includes the introduction of renewable energy technologies
to improve the air quality in Chongqing and minimize GHG emissions while achieving
optimal economic development. Three sub-models (material flow balance model,socio-economic model and energy balance model) and one objective function, gross regional
product (GRP), were included in the simulation. The paper also describes the methodology of
the Input-Output model and puts forwards a comprehensively initial policy analysis.
2. Method
This paper focused on building a comprehensive simulation model system to forecast
environmental improvement potential based on input-output table and balances simulatingsocial, economic and environmental developments. The simulation model consideredsignificant factors, such as GRP growth, energy consumption and the existing individual
sub-sectorial policy targets.
This study utilized an optimization simulation model, comprising dynamic linear LINGO
programming, to evaluate the impact of the renewable energy technologies on gas emission
reduction along with optimized economic development in the research area. The
comprehensive simulation model can precisely estimate the benefits that may result fromsocial, economic, and environment policy decisions.
2.1 Scheme of the Simulation Model2

The simulation model is composed of 3 sub-models (socio-economic model, air emission
model and energy demand-supply model) and one objective function (Figure 1). Chongqing’ssocial and economic activities provide the simulation base and reflect real social andeconomic development. Three economic agents (industry, household, government) wereassumed in this research.
Max GRP

Social-economic
Model

Constraint on SO2 and GHG
emission, energy utilization
SO2 and GHG
emission flow model

Energy demandsupply balance model

Environmental tax

Renewable energy
technology

SO2 and GHG
emission reduction

New energy
substitution

Figure 1. Model framework
There are four big industries in the model. Each industry contains several sectors. We
calculated the input coefficients between economic agents based on “2010 Input-Output tableof Chongqing”.

2.2 Design of the Simulation ModelIn the simulation model, the variables are divided into endogenous (en) and exogenous (ex).
The exogenous parameters are calculated based on current data; the endogenous variables will
be determined by the model structure. The research period is 11 years, from 2010 to 2020.
The selection of the research period was based on the availability of specific governmenttargets for both SO2 and GHG emissions. The objective function of this simulation is tomaximize GRP of the study area and it can be formulated as:t −111 1 Max ∑  GRP (t )t =1  1 + ρ ( ρ = 0.05)

GRP(t ) = V1 X 1 (t ) + V2 X 2 (t ) + V3 X 3 (t ) + V4 X 4 (t )

(1)
(2)

Where, GRP (t) is the gross regional product in term t (en), Xi(t) is the production of eachindustry i in term t (en) and 𝜌 is the social discount rate. Vi is the value-added rate ofindustry i (ex). The objective function is subject to the following constraints.

3

2.2.1 Balance of the Material FlowsIn Formula 3, the left side represents the supply and the right side represents the demand.
Usually, the supply is not lower than the demand. The input coefficient of production iscalculated based on the 2010 input-output table of Chongqing.
4X (t ) ≥ ∑ Aij X j (t ) + Cih (t ) +C i + I i (t ) + E i − M i (t )g(i =1...4)

(3)

j =1

Xi ( t ): commodity of each sector in industry i (en, column vector)

A ij : input coefficients from industry i to industry j (ex, matrices)

Cih (t ): household consumption of each sector in industry i (en, column vector)

Cig : government consumption in each sector in industry i (ex, column vector)
Ii : investment in each sector in industry i (en, column vector)
Ei ( t ): export of each sector in industry i (ex, column vector)

M i ( t ) : import of commodity of each sector in industry i (en, column vector)

2.2.2 Energy Flow Balance
The energy supply in left side is not lower than the energy demand from industries, household
consumption, export and import.
4X e (t ) ≥ ∑ Aei X i (t ) + Ce h (t ) + Ee − M e (t )

(4)

i =1

Where X e (t ) is energy supply and Aei is input coefficient
2.2.3 Value Balance
The left side of the formula is the income and the right side includes expenses. The income ofeach industry is equal to the expenses.
Pi (t )=
X i (t ) P1 (t ) A1i X i (t ) + Pe (t ) Aei X i (t ) + P3 (t ) A3i X i (t ) + Yi h (t ) + δ i X i (t ) + t i X i (t )Pi ( t ) : price rates of each sector in industry i (en, row vector)
Yih (t) : national income of each sector in industry i (en, row vector)

δi : depreciation rate of each sector in industry i (ex, row vector )4(5)τi : indirect tax rate of each sector in industry i (ex, row vector)

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