Energy Demand and Trade in General Equilibrium

Abstract

This paper sheds light on the impact of alternative environmental policies on energy demand, global ${ CO}_2$ emissions, trade, and welfare. For this, we develop an Eaton–Kortum type general equilibrium model of international trade which includes an energy sector. We structurally estimate the key parameters of the model and calibrate it to the data on 31 OECD countries and the rest of the world in the year 2000. The model helps assessing the relative welfare effects under alternative environmental policies. We find that, when carbon spillover effects are absent, taxing energy resources as an input in energy production is preferable to taxing domestic energy production in terms of minimizing ${ CO}_2$ emissions. However, with negative externalities on foreign customers domestic energy output should be taxed to minimize world carbon emissions given a certain level of welfare change for all countries.

Appendix

1.1 Measuring Production Shares

1.1.1 Calculating $\alpha$, $\beta$, $\gamma$

We can estimate production function parameters of the final good producer by using the first-order conditions from Sect. 2 together with data from OECD’s STAN Industry Database. In particular, $\alpha$ can be estimated as the average ratio of total value added in total output according to Eq. (2.12). This ratio is directly observable for 31 OECD countries in the data. We classify the sectors contained in the data into three categories. Intermediate goods are defined as agricultural and manufacturing products, energy is defined as an aggregate of five sectors according to the above classification, and final goods are defined as an aggregate of the following services:

• Community, social, and personal services.

• Construction.

• Finance, insurance, real estate, and business services.

• Transport, storage, and communications.

• Wholesale and retail trade - restaurants and hotels.

To estimate $\alpha$, we take the ratio of value added to total output for 31 OECD countries in 2000. The average value of $\alpha$ in our sample is 0.54. In order to pin down $\beta$ and $\gamma$, we use STAN input–output tables as follows:

$$\begin{aligned} \dfrac{\beta }{\gamma }=\dfrac{L_ip_{qi}q_{ci}}{L_ip_{ei}e_{ci}} \quad \text{ such } \text{ that } \quad \beta +\gamma =1-\hat{\alpha }. \end{aligned}$$

(8.1)

Notice that $L_ip_{qi}q_{ci}$ is the total value of intermediates used in the production of the final good, and $L_ip_{ei}e_{ci}$ is the total value of energy used.¹⁹ Once we fix $\alpha$ at 0.54, we can solve for $\beta$ and $\gamma$ using observations for 31 OECD countries in 2000 as in (8.1). The calculated average values for these two parameters are $\beta =0.34$ and $\gamma =0.12$.

1.1.2 Calculating $\epsilon$, $\nu$, and $\mu$

Intermediate tradable goods can be largely classified into two broad classes:

• Agriculture, hunting, forestry, and fishing.

• Manufacturing.

As in the case of the non-tradable final goods sector, we calculate the average share of value added in total output using STAN input–output tables. The weighted average of $\epsilon$ taken across all industries in the two sectors that we classify as tradables and across 31 OECD countries is 0.32 in the year 2000. To calculate $\nu$ and $\mu$ we pursue the same strategy as in the previous subsection:

$$\begin{aligned} \dfrac{\nu }{\mu }=\dfrac{L_ip_{qi}q_{qi}}{L_ip_{ei}e_{qi}} \quad \text{ such } \text{ that } \quad \nu +\mu =1-\hat{\epsilon }. \end{aligned}$$

(8.2)

This yields $\nu =0.56$ and $\mu =0.12$.²⁰

1.1.3 Calculating $\zeta$, $\xi$, $\chi$

Recall the industry classification for the energy sector from above. The weighted average of value added as a share of output in the five energy sub-sectors in our OECD STAN Industry sample is 0.35. We define the energy resource input asMining and quarrying (energy) usage and calculate $\xi$ and $\chi$ in the same way as the other production parameters:

$$\begin{aligned} \dfrac{\xi }{\chi }=\dfrac{L_ip_{qi}q_{ei}}{L_ip_{g}g_{ci}} \quad \text{ such } \text{ that } \quad \xi +\chi =1-\hat{\zeta }. \end{aligned}$$

(8.3)

Solving (8.3) obtains the parameters $\zeta =0.35$, $\xi =0.18$ and $\chi =0.47$.

1.2 Border Tax Adjustment

In this section, we provide a brief discussion of how border tax adjustment can be implemented in the vein of the model. Recall that $t_{ij}$ and $t^e_{ij}$ denote import tariffs on intermediate and energy goods, respectively. In terms of the model, there is no difference between border tax adjustment and import tariffs from the point of view of implementing a policy to prevent adverse effects of carbon leakage. For example, Elliott et al. (2010) argue that a border tax adjustment would completely eliminate the problem of carbon leakage. The authors agree that calculating the carbon the content of imported goods could be very costly. Hence, a border tax adjustment is likely to be implemented in the form of a simple import tariff levied on the imports from countries that do not comply with environmental regulations.

In our framework, higher import tariffs will inter alia increase the price of intermediate goods. This, in turn, will increase the price of energy goods, reducing energy demand. On the other hand, in Eaton–Kortum type models there is an optimal level of tariffs. Hence, countries may increase their welfare by generating higher import tariff revenues.²¹. Usually, this two-way link between taxes and energy demand is missing, so that taxes can successfully neutralize carbon leakage without affecting ${ CO}_2$ emissions directly.

In this experiment, we gradually increase $t_{ij}$ for Australia and the United States such that counterfactual tariffs are $t'_{ij}=\delta t_{ij}$ where $t_{ij}$ is the benchmark tariff and $\delta =(1.00,1.01,\ldots ,1.50)$. We plot the change in the domestic and world carbon emissions against the change in real welfare in Fig. 5. As in Experiment 1, we use an interval for the value of $\omega$. Notice that a marginal increase in the level of import tariff leads to welfare gains through higher tariff revenues. The same policy, however, leads to an increase in carbon emissions. This is true for both domestic and global ${ CO}_2$ emissions. Higher import tariffs increase the price of intermediate goods. As a consequence, producers substitute away from intermediate goods towards energy goods, thereby increasing domestic energy demand. The latter is translated into higher world energy demand and hence more global ${ CO}_2$ emissions.

Fig. 5

Effect of $t_{ij}$ on ${ CO}_2$ emissions and welfare

Alternatively, consider a counterfactual exercise about energy goods tariffs with $(t^e_{ij})'=\delta t^e_{ij}$ where $t^e_{ij}$ is the benchmark tariff and $\delta =(1.00,1.01,\ldots ,1.50)$ (Fig. 6).

Fig. 6

Effect of $t^e_{ij}$ on ${ CO}_2$ emissions and welfare

Trade in energy is very limited to start with. The average of $\pi ^e_{ii}$ in our sample is 0.96. Hence, an average country in our sample imports only 4 % of energy goods. Our conjecture is that unobservable trade costs are too high for more intense trade in energy goods. This low trade intensity effectively means that both small and large countries experience similar welfare and environmental effects as import tariffs on energy goods are changed. The imposition of higher energy import tariffs increases domestic ${ CO}_2$ emissions. At first glance, this may seem counterintuitive as higher energy import tariffs must increase domestic energy prices and reduce energy demand. Yet, while it is correct that the domestic price of energy $p_{ei}$ rises with $t^e_{ij}$, higher energy tariffs and subsequently higher energy goods prices also increase the price of intermediate goods. The substitution effect towards energy is stronger because intermediate goods have higher production weights compared to energy. Hence, we actually observe an increase in domestic energy demand as a result. This change, however, is small and does not affect world emissions of ${ CO}_2$ significantly.