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«CO2 EMISSIONS EMBODIED IN CHINA’S TRADE AND REDUCTION POLICY ASSESSMENT Tianyu Qi a,b,, Niven Winchester b,Valerie J. Karplus b, Xiliang Zhang a ...»

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Following Böhringer et al. (2011), we adopt a MRIO model to calculate the life-cycle carbon content embodied in production. Both the direct carbon emissions from the combustion of fossil fuel and indirect carbon emissions associated with demand for intermediate non-fossil inputs are captured. We calculate the lifecycle carbon content associated with production of good i in region r as the product of the carbon content per dollar of production, Ayi,r, multiplied by the value of production, yi,r. This product is equal to the sum of direct emissions from the burning of fossil fuel inputs in the production process, Edi,r, and indirect emissions associated with intermediate non-fossil inputs from domestic sources, Eidi,r, and imported sources, Eimi,r, as described by equation (1).

A y i, r  y i, r  E d i, r  E id i, r  E im i, r (1) Direct emissions associated with energy consumption in sectoral production can be easily extracted from the MRIO database. To calculate indirect emissions, we exploit the input-output coefficients in the database.

Indirect emissions from domestic intermediate inputs are calculated as:

–  –  –

where j indexes goods used as intermediate inputs in the production of good i.

Indirect emissions from imported intermediate inputs are the sum of emissions associated with the production of those intermediates and

emissions from international transportation:

–  –  –

where yj,i,s,r is the quantity of imported input j used in the production of good j imported from region s in region r, Atj,r is the per-dollar carbon content of transportation services required to deliver good j to region r, which is

calculated as:

–  –  –

In Equation (4), vtwrt,j,s,r is the value of good j transported from region s to region r by service t (t includes air transport, water transport and land transport), ATrt is the average carbon content of transport service t. In Equation (5), vstt,r and Ayt,r are respectively the quantity of transportation service t and the per dollar carbon content of transport service t supplied by region r.

Equations (1)-(5) represent a system of simultaneous equations, where the lifecycle per-dollar carbon content of each good (Ayi,r) are endogenous variables and other variables are exogenous. Values for exogenous variables are sourced from a MRIO database and the simultaneous equation model is solved iteratively, after assigning intial values for Ayi,r.

Table 1. Sectoral and regional aggregation

–  –  –

Our MRIO model is based on the latest release of GTAP 8 database.

The database is a global economic and energy dataset that includes value flows for 57 sectors and 129 regions in 2007 (Narayanan et al., 2012). The dataset combines individual national energy and economic accounts together with data on bilateral trade flows and CO2 emissions. For our purposes, we aggregate the database to 23 sectors and 27 regions, by aggregating sectors and regions which account for a small proportion of China’s total trade. To focus on embodied emissions, our aggregation identifies three primary energy sectors (Coal, Crude oil, and Gas), Electricity, six energy-intensive sectors (Paper and paper products; Chemical, rubber and plastic products;

Non-metallic minerals; Iron and steel; Fabricated metal products; and Nonferrous metals). Detailed sectoral and regional aggregation is listed in Table 1.

3.2 The CGE Model for Policy Assessment

To assess the impact of current policies on the reduction of tradeembodied carbon emissions in China, we employ a multi-sector, multiregion static CGE model of the global economy. In the model, there are three types of production processes: extraction of primary fuels (crude oil, coal and gas), production of electricity, and other production activities including refined oil, manufacturing and services. Each of the production technologies is captured by a nested constant elasticity of substitution (CES) cost function. Detailed nesting structures for the three production activities are portrayed in Figure 1, where σ is used to denote elasticities of substitution. An important feature of the nesting structure is the ability for firms to substitute among fossil fuels and between aggregate energy and value added. Firms are assumed to compete in perfectly competitive markets.

–  –  –

Figure 1. The nesting structure of production sectors is shown for (a) primary fuels (coal, crude oil and gas), (b) electricity, (c) refined oil and other production.

Final demand by consumers in each region is determined by a series of nested CES functions. The nesting structure splits consumption into an energy composite and other goods and services. Investment is fixed and government consumption is exogenous. Consumers chose their demand profile to maximize their welfare subject to the budget constraint and receive income from payments to capital, labor, and fuel resources (factor income) and tax revenue.

–  –  –

where Ai,r is the Armington composite of good i in region r, Di,r is the domestic variety, and Mi,r is the imported variety, which is a further CES aggregation of imports from different regions. The α are CES share coefficients. The Armington substitution elasticity, δi, between domestic and

the imported goods is given by:

 i  1 / (1   i ) (7) The CGE model is calibrated using the MRIO database (GTAP 8) used to analyze embodied carbon emissions. The model is formulated as a mixed complementarity problem (MCP) using the mathematical programming system (MPSGE) language, which is a subsystem of the General Algebraic Modeling System (GAMS), and solved with the PATH solver to derive the vector of prices that clears the market and the associated demands across all sectors (Mathiesen, 1985; Rutherford, 1995; Rutherford, 1999).

4. Scenarios and Results

In this section, we discuss our scenario design and simulation results.

We first present our analysis of China’s trade-embodied carbon emissions in 2007 using the MRIO model. We then use the CGE model to simulate two policy shocks based on measures outlined in China’s FYP and evaluate the impact on the economy, total emissions, and China’s trade-embodied CO2 emissions.

MRIO calculation of carbon emissions embodied in China’s 20074.1 trade

To derive trade-embodied carbon emissions, we multiple carbon intensities by sector and region (Ayi,r) by China’s export and import values.

Europe, the United States and Japan are China’s largest trade partners, and combined account for more than half of China’s total trade by value. The calculations also reveal that sectoral carbon intensities are much higher than those in Europe, Japan and the US, and also higher than the global average.2 The results also show that domestic intermediate input emissions, which are mainly due to direct and indirect use of electricity, are the largest contributor to lifecycle embodied emissions, rather than direct emissions (Figure 2).

Figure 2. Carbon intensity by sector and by source comparison in 2007

Figure 3 plots China’s scetoral export value shares against sectoral lifecycle carbon intensities. We find that some of the least emissionsintensive sectors have a high value share, while some of the most emissionsintensive sectors barely show up in China’s trade composition. As shown in Figure 3, Electricity (elec) and Gas (gas) production are the two most carbon intensive sectors in China but have very limited trade flows with other regions. Energy intensive sectors, such as Non-metallic minerals (nmm) Fabricated metal products (fmp), and Iron and steel (i_s), have relatively higher carbon intensities but their trade volumes are generally small, together accounting for only 20% of total trade value. Electronic equipment (ele) and Machinery and equipment (ome) account for 22% and 18% of total trade, respectively. However, the carbon intensities of these sectors are relatively low.

We convert among currencies using market exchange rates. If purchasing power parity exchange rates are used, carbon intensities in China are closer to the world average level but remain above those in the US, Europe, and Japan.

Figure 3. China’s sector carbon intensity and trade value in 2007 A comparison of carbon emissions embodied in China’s trade by sector in 2007 is shown in Figure 4.

We found exports of Machinery and equipment and Electronic equipment together account for 34% of China’s exports of embodied carbon, while the energy-intensive sectors combined account for a total of 30%. Textiles and apparel are also significant sources of embodied emissions. These findings reveal that energy intensive sectors are not the primary source of China’s embodied carbon exports, despite the fact that most policies in China target energy-intensive sectors. Meanwhile, exports of mechanical and electronic equipment are encouraged in order to spur development of so-called “high tech” sectors. Overall, the CO2 emissions embodied in China’s net exports in 2007 totaled 1176 million metric tons (mmt), accounting for 22% of China’s CO2 emissions.

Figure 4. CO2 emissions embodied in China’s exports and imports by sector in 2007

4.2 Policy Scenarios and Simulation Results

Using the MRIO analysis of China’s trade embodied CO2 emissions in 2007 as the reference scenario, we use a static CGE model to simulate two representative policy scenarios: 1) an economic rebalancing policy (ER) scenario and 2) a scenario in which additional export tariffs are imposed on energy-intensive industries (ET), simulating the effect of reduced incentives to export. The objective is to evaluate the impact of the two policies on China’s trade and CO2 emissions.

Scenario 1: Economic Rebalancing Policy (ER)

Under the Twelfth FYP, China’s policymakers aim to encourage adjustment in the country’s economic structure to reduce reliance on heavy industry and increase the contribution of services and less energy intensive industries, while simultaneously encouraging domestic consumption. As part of our simulation of an economic rebalancing policy, we develop two variants of our first scenario to simulate the impact of a policy aimed at rebalancing the economy (ER1) and the effect of combining it with a domestic demand expansion policy (ER2). Detailed assumptions for each variant of Scenario 1 are listed in Table 2.

Scenario description – economic rebalancing with and without domestic Table 2.

demand expansion.

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The reference case reflects China’s economic situation in 2007 as described by the GTAP 8 database. China’s rebalancing target in 2015 is reflected in the targets for sectoral share of GDP for each scenario, and is based on the targets in China’s Twelfth FYP (State Council of China, 2011) and in the report of the Development Research Center of the State Council (Xinhua, 2010). In ER1, we only consider the economic rebalancing policy and we assume that China’s trade surplus remains unchanged from the reference scenario. However, in ER2, we decrease the trade surplus by half, capturing the situation in which China would spend more money on domestic consumption, rather than investing abroad. The implementation of the economic rebalancing targets is achieved by imposing endogenous taxes or subsidies on all sectors within each sectoral group to achieve the GDP shares in Table 2. In practice, a fiscal policy approach would be a (potentially more) important approach to stimulate domestic consumption.

However, in our analysis, we are not focused on specific policy comparisons, but are interested in providing insight into the impact of a generic rebalancing policy. We are further interested in quantifying the effects of the policy on carbon emissions embodied in China’s trade. The change in China’s trade surplus is simulated as an exogenous shock to the capital account balance.

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