The effect of energy end-use efficiency improvement on China’s energy use and CO₂ emissions: a CGE model-based analysis

Abstract

With its rapid economic growth, China is now confronted with soaring pressure from both its energy supply and the environment. To deal with this conflict, energy end-use efficiency improvement is now promoted by the government as an emphasis for future energy saving. This study explores the general equilibrium effect of energy end-use efficiency improvement on China’s economy, energy use, and CO₂ emissions. This paper develops a static, multisector computable general equilibrium model (CGE) for China, with specific detail in energy use and with the embodiment of energy efficiency. In order to explore the ability of subsidizing non-fossil-generated electricity on moderating potential rebound effects, in this model, the electricity sector was deconstructed into five specific generation activities using bottom–up data from the Chinese electricity industry. The model is calibrated into a 16-sector Chinese Social Accounting Matrix for the year 2002. In the analysis, seven scenarios were established: business as usual, solely efficiency improvement, and five policy scenarios (taxing carbon, subsidized hydropower, subsidized nuclear power, combination of taxing carbon and subsidized hydropower, combination of taxing carbon and subsidized nuclear power). Results show that a sectoral-uniform improvement of energy end-use efficiency will increase rather than decrease the total energy consumption and CO₂ emissions. The sensitivity analysis of sectoral efficiency improvement shows that efficiency improvements happened in different sectors may have obvious different extents of rebound. The three sectors, whose efficient improvements do not drive-up total national energy use and CO₂ emissions, include Iron and Steel, Building Materials, and Construction. Thus, the improvement of energy end-use efficiency should be sectoral specific. When differentiating the sectoral energy-saving goal, not only the saving potential of each sector but also its potential to ease the total rebound should be taken into account. Moreover, since the potential efficiency improvement for a sector over a certain period will be limited, technology measures should work along with a specific policy to neutralize the rebound effect. Results of policy analysis show that one relatively enhanced way is to combine carbon taxing with subsidized hydropower.

Appendix. A Description for the basic model structure of CEEPA

A.1 Production module

The production module specifies the production activity in each sector. Non-joint production is assumed in this model, i.e. supposing that an industry produces one, and only one, distinct commodity. The inputs in each sector include labor, capital, energy and other intermediate inputs, following a five-level nested constant elasticity of substitute (CES) function.

At the top level, sectoral output is constitutive of different intermediate inputs and capital-energy-labor composition following Leontief function (see Eq. 7), i.e., supposing that there is no substitution among different intermediate inputs, nor between intermediate inputs and capital-energy-labor composition.

$$X_i = \min \left( {\frac{{Z_{1,i} }}{{\alpha _{1,i} }},\frac{{Z_{2,i} }}{{\alpha _{2,i} }}, \ldots ,\frac{{Z_{n,i} }}{{\alpha _{n,i} }},\frac{{{\text{KEL}}_i }}{{\alpha _{kel,i} }}} \right)$$

(7)

Where,

X _i :

∼total output of sector i

Z _j,i :

∼intermediate input of commodity j in sector i

KEL _i :

∼composite capital-energy-labor input in sector i

α _j,i :

∼the direct requirement of sector i on sector j for per unit output of sector i

α _kel,i :

∼the direct requirement of sector i on capital-energy-labor composition for per unit output of sector i.

At the second level, this model follows the structure used in many energy CGE models, and assumes that Labor (L), Capital (K), and Energy (E) constitute capital-energy-labor composition using the structure of (K/E)/L. That is, the relationship between energy and capital is assumed to be quasi-complementary, the input of energy usually accompany with corresponding input of capital such as equipment; while the substitute elasticity between capital and labor, and that between energy and labor is larger. The input of capital or energy usually accompanies the substitute for labor (Wu and Xuan 2002). The production function at this level is show as Eq. 8.

$${\text{KEL}}_i = A_{{\text{KEL}},i} \left( {\alpha _{{\text{KE}},i} KE_i^{\rho _{{\text{KEL}},i} } + \left( {1 - \alpha _{{\text{KE}},i} } \right)L_i^{\rho _{{\text{KEL}},i} } } \right)^{\frac{1}{{\rho _{{\text{KEL}},i} }}} $$

(8)

Where,

KE _i :

∼composite capital-energy input of sector i

L _i :

∼labor input of sector i

A _KEL,i :

∼shift parameter in CES function (capital-energy-labor)

α _KE,i :

∼CES share parameter of capital-energy composition in capital-energy-labor composition

ρ _KEL,i :

∼substitution parameter in capital-energy-labor aggregate function

At the third level, capital-energy composition is constitutive of capital and energy composition following a CES function. As for the energy composition, since the production of electricity will always consume fossil fuels, the substitution elasticity between electricity and fossil fuels should be smaller than those within fossil fuels (Wu and Xuan 2002). Therefore, at the fourth level, the energy composition is constitutive of electricity input and fossil fuel composition. At the lowest level, fossil fuel composition is constituted by coal, crude oil, petroleum and natural gas, following Cobb and Douglas (1928) function (see Eq. 9).

$${\text{Fossil}}_i = A_{{\text{Fossil}},i} \cdot \prod\limits_{{\text{fe}}} {{\text{FoF}}_{{\text{fe}},i}^{\beta _{{\text{FoF}},{\text{fe}},i} } } $$

(9)

Where,

FoF_fe,i :

∼input of fossil fuel fe of sector i

A _fossil,i :

∼shift parameter in CES function (fossil fuel composition)

β _FoF,fe,i :

∼share parameter of fossil fuel fe in fossil fuel composition in sector i

An exception here is the production function for the petroleum refining sector. Referring to the assumption in MIT-EPPA model (Paltsev et al., 2005), since crude oil is the most important raw material in the petroleum refining process, then in the production function of this sector, crude oil is taken out from the fossil fuel composition and placed in the top level.

A.2 Income and expenditure module

A.2.1 Household income and expenditure

Household income mainly comes from labor income and profit distribution from enterprises. After paying household income tax and receiving various transfers from government and overseas, the households get disposable income. One part of household disposable income is spent on saving, and the other part on consumption of various goods.

Household saving is obtained by multiplying household disposable income with marginal saving tendency. Household consumption for various goods is described with a linear expenditure system (LES) function (see Eq. 10).

$${\text{CDh}}_{i,h} = \frac{{{\text{cles}}_{i,h} \cdot \left( {1 - {\text{mps}}_h } \right) \cdot {\text{YD}}_h }}{{{\text{PQ}}_i }}$$

(10)

Where,

CDh _i,h :

∼consumption of commodity i by household h

PQ _i :

∼composite price of commodity i (imports and domestic products)

YD _h :

∼disposable income of household h

cles _i,h :

∼consumption share of commodity i in the total consumption of household h

mps _h :

∼marginal saving rate of household h

A.2.2 Enterprise income and expenditure

The major source of enterprises income is the return on capital.

After paying enterprise income tax enterprises obtain net profit after tax. On part of the net profit after tax is transferred to the households as profit distribution, and the other part is kept as enterprise saving (see Eq. 11).

$${\text{EnSav}} = {\left( {1 - {\text{e\_h}}} \right)} \cdot {\left[ {{\left( {1 - {\text{etax}}} \right)} \cdot {\text{YK}} - {\text{SBT}} \cdot {\text{ER}}} \right]} + {\text{GtoE}} \cdot {\text{PIndex}}$$

(11)

Where,

EnSav:

∼enterprise saving

ER:

∼exchange rate

GtoE:

∼government’s transfer payment to enterprise

YK:

∼total capital income

PIndex:

∼GDP price deflator

SBT:

∼capital income distributed to enterprises from ROW (rest of world)

e_h:

∼share of total distributed profits from enterprises to households

etax:

∼enterprise income tax

A.2.3 Government income and expenditure

Government income is constitutive of various tax and transfers from other countries/regions (ROW). Government expenditure includes government consumption, transfers to households and enterprises, and export rebate. Government consumption is described by the LES function (see Eq. 12). In a given period the difference between government income and expenditure, forms government saving (see Eq. 13).

$${\text{GD}}_i = {\text{gles}}_i \cdot {\text{GdTot}}$$

(12)

$$\begin{array}{*{20}l}{\sum\limits_i {{\text{PQ}}_i {\text{ $ \times $ GD}}_i {\text{ + GovSav + GtoH $ \times $ PIndex + GtoE $ \times $ PIndex}}} } \hfill \\{ = {\text{IndTax + Tariff - ExSub + TotHTax + etax $ \times $ YK + WtoG $ \times $ ER}}} \hfill \\\end{array} $$

(13)

Where,

GD _i :

∼government consumption of good i

GovSav:

∼government saving

IndTax:

∼total indirect tax

Tariff:

∼total tariff

ExSub:

∼total export rebate

TotHTax:

∼total household income tax

WtoG:

∼transfers to government from ROW

Gdtot:

∼total government consumption

gles _i :

∼share of good i in total government consumption

A.3 Foreign trade module

In this study we adopt the Armington (1969) assumption, and assume that there is imperfect substitutability between imports and domestic output sold domestically. The commodity that is supplied domestically is composed of domestic and imported commodities following a CES function (see Eq. 14 and 15).

$$Q_i = A_{Q,i} \cdot \left[ {\alpha _{M,i} \cdot M_i^{\rho _{Q,i} } + \left( {1 - \alpha _{M,i} } \right) \cdot D_i^{\rho _{Q,i} } } \right]^{{1 \mathord{\left/{\vphantom {1 {\rho _{Q,i} }}} \right.\kern-\nulldelimiterspace} {\rho _{Q,i} }}} $$

(14)

$$\frac{{M_i }}{{D_i }} = \left[ {\frac{{\alpha _{M,i} }}{{1 - \alpha _{M,i} }} \cdot \frac{{{\text{PD}}_i }}{{{\text{PM}}_i }}} \right]^{\sigma _{Q,i} } $$

(15)

Where,

Q _i :

∼domestic sale of Armington composite good i

M _i :

∼import of sector i

D _i :

∼domestic good i sold domestically

PD _i :

∼price of good i produced and sold domestically

PM _i :

∼domestic price of import good i

A _Q,i :

∼shift parameter in Armington function (CES)

ρ _Q,i :

∼substitution parameter in Armington function for import

α _M,i :

∼share parameter in Armington function (CES)

σ _Q,i :

∼substitution elasticity between import and domestic production

As for export, this model uses a Constant Elasticity Transformation (CET) function to allocate total domestic output between exports and domestic sales (see Eq. 16 and 17).

$$X_i = A_{Ex,i} \cdot \left[ {\alpha _{Ex,i} \cdot E_i^{\rho _{Ex,i} } + \left( {1 - \alpha _{Ex,i} } \right) \cdot D_i^{\rho _{Ex,i} } } \right]^{{1 \mathord{\left/{\vphantom {1 {\rho _{Ex,i} }}} \right.\kern-\nulldelimiterspace} {\rho _{Ex,i} }}} $$

(16)

$$\frac{{E_i }}{{D_i }} = \left[ {\frac{{1 - \alpha _{Ex,i} }}{{\alpha _{Ex,i} }} \cdot \frac{{PE_i }}{{PD_i }}} \right]^{\sigma _{Ex,i} } $$

(17)

Where,

E _i :

∼export of good i

PE _i :

∼domestic price of export good i

A _EX,i :

∼shift parameter in transformation function (CET)

ρ _EX,i :

∼substitution parameter in CET function for export

α _EX,i :

∼share parameter in transformation function (CES)

σ _EX,i :

∼substitution elasticity between export and domestic sales

A.4 Investment module

Total investment in the model is characterized in two ways: circulating capital investment and fixed investment. Adjusting total fixed investment with corresponding sectoral shares, we identified fixed investment supplied to each sector, which serves to clear the capital market. Further multiplying the fixed investment supplied to each sector with the composition matrix of fixed capital, we established fixed investment supplied by each sector, which serves to clear the commodity market. Major functions in this sub-module are shown by Eq. 18–20.

$${\text{Dk}}_i \cdot {\text{Pk}}_i = \mu _i \cdot {\text{FxdInv}}$$

(18)

$${\text{Pk}}_i = \sum\limits_j {sf_{j,i} {\text{ $ \times $ PQ}}_j } $$

(19)

$${\text{ID}}_i = \sum\limits_j {sf_{i,j} \cdot {\text{Dk}}_j } $$

(20)

Where,

Dk _i :

∼fixed capital investment supplied to sector i

Pk _i :

∼price of fixed capital in sector i

FxdInv:

∼total fixed capital investment

ID _i :

∼fixed capital investment supplied by sector i

μ _i :

∼share of sector i in total fixed capital investment

sf _i,j :

∼composition matrix coefficient of fixed capital for sector j from investment good i

A.6 Model closure

Model closure identifies the borderline of the model by differentiating the exogenous and endogenous variables (Wu and Xuan 2002). This model considers three principles of closure, namely: government budget balance, foreign trade balance, and invest-saving balance.

When considering the government budget balance, this model adopts the principle of government consumption exogenous, while government saving is endogenous. For the foreign trade balance, this model adopts the principle of foreign saving exogenous, and the exchange rate endogenous. For the invest-saving balance, this model follows the principle of “neoclassical closure,” and assumes that all the saving is transformed into investment, and that total investment equals total saving endogenously; thus the model is saving-driving.

A.7 Market clearing

In this model, only commodity and capital markets are cleared.

The clearance of the commodity market requires that the gross supply of a commodity must equal the gross demand for that commodity (see Eq. 21). The gross supply of a commodity is the Armington (1969) composition of domestic and imported goods. The gross demand for a commodity consists of intermediate demand and various final demands (including household consumption, government consumption, fixed and circulating capital investment demand).

$$Q_i = {\text{Int}}_i + \sum\limits_h {{\text{CDh}}_{i,h} + {\text{GD}}_i + {\text{ID}}_i + {\text{Dst}}_i } $$

(21)

Where,

Int _i :

∼supply of intermediate goods by sector i

Dst _i :

∼stock change of good i

This model assumes that the capital market could achieve full sufficient adjustment under external shock. The supply of capital is set exogenously; the allocation of which are adjusted among sectors according to the sectoral return of equity. Market clearing requires that the total capital demand from all the sectors equals the exogenous total supply of capital.

For the labor market, here we referred to the assumption of Glomsrød and Wei (2005), assuming that in a lengthy time in the future the labor supply in China would be relatively surplus. Therefore in the model there is no clearance of labor market, but assumes that the wage rate will keep rigid, also there exists involuntary unemployment in the labor market.