Advertisement

Natural Hazards

, Volume 99, Issue 3, pp 1415–1430 | Cite as

Socioeconomic impacts of a shortage in imported oil supply: case of China

  • Mei-Mei Xue
  • Gang Wu
  • Qian Wang
  • Yun-Fei Yao
  • Qiao-Mei LiangEmail author
Original Paper
  • 390 Downloads

Abstract

Oil is an essential and important energy source and is related to energy security and national strategy. Based on a recursive dynamic computable general equilibrium model, this study examines the potential impacts on the major socioeconomic indices in China, which includes economic growth, price level, employment, household welfare, and production activity, under different imported oil shortage scenarios. Results show that oil shortage has negative impacts on China’s economic growth; if all the extra proceeds from domestic oil price increase is assigned to government budget for compensating investment loss, the negative impacts on GDP will mainly stem from its negative impacts on total consumption; the decrease in labor income plays an obviously greater role in the decrease in total consumption than the effects of an increase in CPI. The negative impacts on rural households are greater than urban households. The profit of most production sectors, and the export of almost all sectors, will be negatively influenced.

Keywords

Oil shortage Socioeconomic impacts Computable general equilibrium China 

1 Introduction

Oil plays an essential and supportive role in modern economic development. Anything wrong with oil supply might eventually ripple throughout the economy of a country/region and impact global economic growth, inflation, employment, etc. (Segal 2007; Shi and Sun 2017).

Being a net oil exporter until 1996, China was not obviously influenced in those major oil crises. However, China continues to increase its oil import and becomes the second largest oil importer in the world, with narrowing the gap with the largest oil USA (IEA 2017) and a continuously increasing dependency on international oil market. China’s oil dependency broke through the 50% international warning line since 2009 (Wei et al. 2012) and was approaching 67.4% in 2017 as shown in a report of CNPC (China National Petroleum Corporation) Economics and Technology Research Institute early this year (CNPC 2018). Therefore, sensitivity of China’s economy to international oil supply shocks will increase obviously.

For a country, shocks from international oil supply may present in the forms of an increase in international oil price and/or a shortage in imported oil supply. Socioeconomic impact of oil price fluctuation has been long receiving much attention. Up to now, there have been many studies addressing problems such as impacts of oil price fluctuation on major macroeconomic indices of a country/region including GDP, inflation, trade balance (Aydın and Acar 2011; Basher et al. 2016; Du et al. 2010; Farzanegan and Markwardt 2009), impacts of oil price shocks on the development of certain industries (Cha and Bae 2011; Yeoman et al. 2007), comparing the macroeconomic effects of oil price shocks in different countries (Cuñado and Pérez de Gracia 2003; Korhonen and Ledyaeva 2010; Peersman and Van Robays 2012), the role monetary policy plays in the macroeconomic effects of oil price shocks (Bachmeier 2008; Lee et al. 2001), the impact of oil price shocks on stock market (Broadstock et al. 2014; Fayyad and Daly 2011; Jiménez-Rodríguez 2015; Kang et al. 2015; Papapetrou 2001; Zhang and Li 2016), the impact of oil price shock on capital accumulation (Vásconez et al. 2015).

Existing studies about the socioeconomic impacts of oil quantity shocks are much less than those focusing on oil price shocks and mainly focus on the relationship between oil supply and GDP or several other socioeconomic indices. For example, Stern (1993) examined the causal relationship between GDP and energy use for the period 1947–1990 in the USA. Roughly assuming the relationship between percent decline in world oil supply and percent decline in world GDP to be 1:1, Hirsch (2008) discussed the potential impacts of different maximum world oil production scenarios on world GDP. Sun et al. (2014) found that the GDP loss in China is about 0.37% when the oil import risk is 10%, and the impact of oil import risk spreads along the value chain, i.e., the impact on service sector is much lower than that on mining sector. Based on related literature reviews, Li et al. (2018) uses CGE to examine the impact of subsidy removal on oil and gas in Malaysia. Tverberg (2012) stated that it is likely that reduced oil supply would lead to reduced or negative economic growth. Wu et al. (2012) applied a dynamic programming model to quantitatively analyze the shocks of different oil shortage events on international oil price and their impacts on China’s GDP. With a two-period model, Bai et al. (2012) found that higher tariff rate and lower stockpiling size are recommended to decrease the country’s vulnerability to supply disruptions, so that to minimize potential social welfare, macroeconomic and policy loss associated with supply disruptions. Lin et al. (2012) simulated the impact of a hypothetical 50% supply disruption for crude oil on the economy of Taiwan and found potential real GDP loss of 0.0093% (imported from Nigeria) to 1.0661 (imported from Saudi Arabia). They also found that the effect of exogenous energy shocks on the economy will be partially absorbed by the internal adjustment mechanism of the economy under a specific emission reduction target.

It is noted that, although an increase in international oil price and a shortage in imported oil supply might lead to similar situation in the domestic market of a country with higher oil price and lower oil demand, their economy-wide impacts could be quite different due to the different incidence of oil price premium. That is, the windfall profit from a higher oil price will be taken by oil exporters in the case of international oil price increase, while will be taken by the domestic companies or government of oil importers in the case of oil supply shortage. Because oil supply shortage can affect a country’s terms of trade which can affect its competitiveness (Vásconez et al. 2015), and even energy security, for example, the U.S. Strategic Petroleum Reserve (SPR) was established in 1975 to mitigate major oil supply disruptions and to deter the use of energy as a geopolitical “weapon” (Scheitrum et al. 2017). Therefore, even with many studies focusing on the impacts of oil price changes, it is still necessary to strengthen studies focusing on the impacts of oil supply shortage, especially for the case of China.

On the one hand, the present oil import structure in China is highly concentrated: 43% of the imported oil comes from Middle East with a relatively turbulent situation. On the other hand, the current strategic petroleum reserve level in China is still relatively low, obviously lower than the required scale proposed by International Energy Agency (IEA) for its member states, i.e., a strategic petroleum reserve equivalent to at least 90 days of net imports. Therefore, from an economy-wide perspective, this study aims to contribute to existing studies by exploring the possible impacts of different oil shortage scenarios on the major socioeconomic indices in China.

The rest of the paper is organized as follows. Section 2 introduces the model used in this study. The setting of oil shortage scenarios is illustrated in Sect. 3; major results and discussions are presented in Sect. 4. Sensitivity analyses are presented in Sect. 5. Finally, main conclusions and further work required to improve the current study are presented in Sect. 6.

2 Methodology

2.1 Model framework

This study is based on the China Energy and Environmental Policy Analysis (CEEPA) model. CEEPA is a multi-sector recursive dynamic computable general equilibrium (CGE) model developed by us. Deriving from the general equilibrium theory of Walras (1969), the CGE model uses a group of simultaneous equations to describe the behaviors of different agents and their interactions in macroeconomic systems. CEEPA follows the basic notion of a general CGE model and assumes that two types of agents are included in the economic system, i.e., producers and consumers. Producers make the optimal decisions about the supply volume under the restriction of resources and technology, according to the principle of revenue maximization. Consumers decide their optimal demand volume under the restriction of budget, based on the principle of utility maximization principle. The only signal in the system guiding the behaviors of each agent is price, and a set of equilibrium prices exist in the system. The equilibrium prices make the optimal supplies equal the optimal demands of all the commodities and factors, thus making the whole economy reach a steady equilibrium status. The framework of CEEPA is illustrated in Fig. 1 (Liang and Wei 2012).
Fig. 1

General framework of CEEPA (Liang and Wei 2012)

In CEEPA, consumers are divided into households, enterprise, and government to reflect their different roles in the economic system. Different types of consumers are interacting with each other through taxes, transfer payments, subsidies, etc. Moreover, considering the current energy- and emission-intensive international trade structure of China, a foreign account was included, making CEEPA an open model. CEEPA is composed of five basic sub-modules, i.e., production, income, expenditure, investment and foreign trade sub-module. Detail introductions about CEEPA including equations, parameters and variables please refer to Liang et al. (2014).

2.2 Introducing shocks from imported oil supply shortage into CEEPA

2.2.1 Recognizing the shortage in imported oil supply

In a standard CGE model, adopting the Armington assumption, the import quantum of a certain commodity by a country is co-decided by its domestic total demand, as well as the relation between its domestic selling price and import price, following a constant elasticity of substitute (CES) function, as shown in Eq. 1.
$$\it M_{i} = A_{i}^{{\sigma_{i} - 1}} \cdot \alpha_{i}^{{\sigma_{i} }} \cdot \left( {\frac{{{{PQ}}_{i} }}{{{{PM}}_{i} }}} \right)^{{\sigma_{i} }} \cdot Q_{i}$$
(1)
where \(M_{i}\)—import quantum of commodity i; \(\it {\text{PM}}_{i}\)—import price of commodity i expressed with domestic currency; \(Q{}_{i}\)—domestic sale of commodity i; \(\it {\text{PQ}}_{i}\)—domestic selling price of commodity i (imports and domestic products); \(A_{i}\)—shift parameter in Armington function (CES) for commodity i; \(\sigma_{i}\)—substitution elasticity between imported and domestic commodity i; \(\alpha_{i}\)—share parameter in Armington function (CES) for commodity i.
When a shortage in world oil supply happens, there will be an upper bound on the amount of oil available for a country to import. It is assumed here that the percent decline in imported oil supply to China equals the average percent decline in world oil supply, as shown in Eq. 2.
$$M_{\text{oil}} \le M0_{\text{oil}} \cdot (1 - \beta )$$
(2)
where \(M0_{\text{oil}}\)—oil import in baseline scenario. \(\beta\)—average percent decline of world oil supply under oil crisis.

2.2.2 Addressing windfall profit

It is worth noticing that, although this study separates the quantity and price shocks of international oil supply, thus the increase in international oil price is not considered here, oil shortage will, however, inevitably lead to the increase in domestic oil price, which could generate extra proceeds. Given the fact that currently all the major large-scale oil companies in China are state-owned, it is assumed that under all scenarios this part of extra proceeds from increased domestic oil price is assigned to government budget as windfall profit tax, as shown in Eqs. 3 and 4.
$$\it {\text{PM}}_{\text{oil}} = \overline{{{\text{PWM}}_{\text{oil}} }} \cdot (1 + \tau_{\text{oil}} + \delta_{\text{oil}} ) \cdot \varepsilon$$
(3)
$$ G_{\text{Income}} = {{Ind}}_{\text{Tax}} + {{Tariff}} + H_{\text{Tax}} + E_{\text{Tax}} + W\,{\text{to}}\,G \cdot \varepsilon + \overline{{{{PWM}}_{\text{oil}} }} \cdot \varepsilon \cdot \delta_{\text{oil}} \cdot M_{\text{oil}}$$
(4)
where \(\it \overline{{{\text{PWM}}_{\text{oil}} }}\)—world price of imported oil; \(\tau_{\text{oil}}\)—tariff rate on imported oil; \(\varepsilon\)—exchange rate; \(\delta_{\text{oil}}\)—windfall profit tax rate on oil; \(G_{\text{Income}}\)—government income; \(\it {\text{Ind}}_{\text{Tax}}\)—indirect tax revenue; \(\it {\text{Tariff}}\)—tariff revenue; \(H_{\text{Tax}}\)—household income tax revenue; \(E_{\text{Tax}}\)—enterprise income tax revenue; \(W\,{\text{to}}\,G\)—transfers to government from the rest of the world.

2.3 Data source and parameter calibration

The database of this model is a 24-sector Social Accounting Matrix (SAM) for China, which was developed based on input–output Table 2007 (NBS 2009) and miscellaneous yearbooks and researches (CSFB 2008; Fu 2008; GAC 2009; NBS 2008, 2010; SAT 2011).

The parameters in the model include exogenous and endogenous parameters. The endogenous parameters are determined by calibration. The setting of exogenous parameters in CEEPA refers to related research and with our own adjustment, as shown in Table 1.
Table 1

Major exogenous parameters in CEEPA and data source

Parameters

Data source

Miscellaneous substitute elasticities

Wu and Xuan (2002)

Potential carbon emission factor of primary energy

IPCC (2006)

Fraction of oxidized carbon

Xue (1998)

Composition matrix of fixed capital

Wang (2003)

3 Oil shortage scenarios

Corresponding to the three major historical oil crises, three oil shortage scenarios are set in this study, as shown in Table 2.
Table 2

Oil shortage scenarios

Scenario

Description

The first oil crisis scenario (Ocris-1)

Imported crude oil supply reduced by 4.9%

The second oil crisis scenario (Ocris-2)

Imported crude oil supply reduced by 4.5%

The third oil crisis scenario (Ocris-3)

Imported crude oil supply reduced by 0.31%

The medium oil crisis scenario (Ocris-M)

Imported crude oil supply reduced by 2.61%

According to the assumptions in Sect. 2.2.1, in each scenario it is assumed that the variation of imported oil supply to China equals the average variation of world oil supply in the corresponding oil crisis. Here the average variation in world oil supply in each historical oil crisis was calculated based on BP statistics (BP 2008), using Eq. 5.
$$\it \beta_{s} = \left( {\frac{{{{OP}}_{t,s} }}{{{{OP}}_{t - 1,s} }} - 1} \right)*100$$
(5)
where \(\it {\text{OP}}_{t,s}\)—oil production in year t under the sth world oil crisis.

t—the first year when oil production began to decline under the sth world oil crisis. For the first, second, and third world oil crisis, t corresponds to the year 1975, 1980, and 1991, respectively. Since the reductions in imported crude oil supply in first two oil crisis are similar, in this study a medium oil crisis scenario is designed to explore the socioeconomic impacts of a medium shortage in imported crude oil supply, and its reduction value is set as the middle value of the maximum and the minimum reduction.

4 Results and discussion

A reference scenario was run where no oil shocks happened. Then the model was rerun to simulate the four oil shortage scenarios. The following illustrates the major results expressed in variations from the corresponding reference values. Table 3 shows the variations of major macroeconomic indices under different oil shortage scenarios.
Table 3

Variations of major macroeconomic indices under different oil shortage scenarios (%)

Indices

Ocris-1 (4.9%)

Ocris-2 (4.5%)

Ocris-3 (0.31%)

Ocris-M (2.61%)

Real GDP

− 0.068

− 0.063

− 0.004

− 0.036

Total investment

− 0.093

− 0.085

− 0.006

− 0.048

Total consumption

− 0.047

− 0.043

− 0.003

− 0.025

Consumer price index (CPI)

0.021

0.019

0.001

0.011

Urban CPI

0.022

0.020

0.001

0.011

Rural CPI

0.019

0.017

0.001

0.010

Employment

− 0.136

− 0.125

− 0.009

− 0.072

Urban household welfarea

− 0.072

− 0.066

− 0.005

− 0.038

Rural household welfarea

− 0.109

− 0.100

− 0.007

− 0.058

aHere welfare is described with Hicksian equivalent variation (EV), unit: billions of RMB Yuan, 2007

4.1 Impacts on economic growth

Oil shortages have negative impacts on GDP, with an extent increasing with the shortage extent. As shown in Table 3, under any of oil shortage scenarios, China’s GDP decreases compared to the reference value, and the decreasing rate increases with the shortage extent: the amount of imported oil shortage under scenario Ocris-1, scenario Ocris-2, and scenario Ocris-3 is 1.88 times, 1.72 times, and 0.12 times of that under scenario Ocris-M, respectively, while GDP loss under scenario Ocris-1, scenario Ocris-2, and scenario Ocris-3 is 1.90 times, 1.74 times, and 0.12 times of that under scenario Ocris-M, respectively.

The shocks of oil shortage to GDP mainly come from its negative impacts on total investment and total consumption. The GDP index discussed here is the real GDP calculated with an expenditure approach, consisting of total expenditure on final consumption, total capital formation and net export of goods and services. According to the closure principle of foreign trade balance in this study, foreign saving is considered as exogenous, in all scenarios the net export of goods and services will be fixed at the corresponding reference value. Therefore, oil shortages under all four scenarios will influence GDP through their impacts on total consumption and total investment. It can be seen from Table 3 that, under these scenarios, the impacts of oil shortage on total investment and total consumption are all negative. The decreasing rates of total investment are obviously greater than that of total consumption under all scenarios, thereby the decrease rates of the real GDP are fell in between them.

The negative impacts of oil shortage on total investment mainly come from the decrease of household and enterprise saving. According to the closure principle of invest-saving balance in this study, total investment is transformed endogenously from total saving. Besides, foreign saving is exogenous in this study, oil shortage impacts total saving through its other three components, i.e., enterprise saving, household saving, and government saving. Model results show that, both return on capital and labor income decrease under all four scenarios, thus enterprise saving and household saving reduce obviously from their reference values. Under all scenarios, windfall profit tax income from increased domestic oil price is assigned to government budget, government saving will increase from its reference level. Due to a much smaller proportion of the government saving in total invest than that of the other two types of saving, it fails to offset the decrease in the other two types of saving. Thus, the total investment will decrease, as shown in Table 3.

The negative impacts of oil shortage on total consumption come from both the rising prices and the decreasing disposable income. Since this model adopts the closure principle of government consumption set exogenous, and household consumption dominates total consumption (the ratio of household consumption over total consumption is 73.5% in reference scenario), in all scenarios oil shortage impacts total consumption mainly through its impact on household consumption. Household consumption is co-decided by household disposable income and the level of consumer price. Moreover, it is positively correlated with the former, while negatively correlated with the latter. Results show that, under all scenarios, oil shortage will lead to the increase in domestic crude oil price and refined oil price and further lead to the decrease in labor demand and enterprise profits, and therefore both urban and rural household disposable income will decline compared to the reference level. CPI increases to some extents under all scenarios. Integrated effects from price and income depress both urban and rural household consumption under all scenarios.

4.2 Impacts on CPI

Oil shortages raise CPI, with the increase of urban household price index greater than the rural one. As shown in Table 3, CPI rises from the reference value under all four scenarios, with the rising scope increasing with the shortage extent. Results also show that the extent to which oil shortage impacts CPI is different between urban and rural households: under scenario Ocris-1, urban and rural CPI increase by 0.022% and 0.019%, respectively; under the scenario Ocris-2, urban and rural CPI increase by 0.020% and 0.017%, respectively; under the scenario Ocris-3, urban and rural CPI increase by 0.001% and 0.001%, respectively; under the scenario Ocris-M, urban and rural CPI increase by 0.011% and 0.010%, respectively.

Shocks of oil shortage to CPI mainly transfer through the price rise of refined oil, transportation, agriculture, clothes, and food, as well as the price decline of service. The level of CPI is co-decided by the prices of various consumption goods and household consumption structure. Household consumption structure refers to the proportional relation of different consumption goods in household total consumption. Results show that, under all scenarios, oil shortage will cause the price rise of all consumption goods except that of other heavy industry products and service products. Though the prices of these two types product reduce just a little bit in general, Service products occupy a large ratio in household consumption (its ratios over rural and urban household consumption are about 39.5% and 46.4%, respectively, in reference scenario). Therefore, it has a big contribution in the variation of CPI, but not enough to offset the upward driving effects of other prices.

Among all the consumption goods with a rising price, the greatest upward driving effect on national CPI comes from the price rise of refined oil, followed subsequently by that of transportation, agriculture, clothes, and food. Under all scenarios, the price rise of these five types of goods in a whole is responsible for about 80% of national CPI increase:

The price rise of refined oil is responsible for more than 25% of national CPI increase under all scenarios, mainly because of the obviously greater increasing amplitude: the ratio of refined oil expenditure over total household consumption is very small (about 0.8% in reference scenario), while the increasing rates of refined oil price under all scenarios are obviously greater than the other consumption goods, being about five times larger than the second greatest increasing rates under all scenarios.

The price rise of food and agriculture is responsible for about 10% and 13% of national CPI increase under all scenarios, mainly because of their important position in household consumption structure. Under all scenarios, the increasing rates of food and agriculture price are fairly small in all consumption goods with a rising price, while the ratios of food and agriculture expenditure over total household consumption are the second and the third largest (about 17.7% and 11.9% in reference scenario).

The price rise of transportation and clothes is, respectively, responsible for about 15% and 12% of national CPI increase under all scenarios, not only because of the relatively higher increasing rates of these two types of goods (under all scenarios the increasing rates of transportation and clothes ranks second and fourth, respectively, of the 19 household consumption goods), but also because of their relatively important positions in household consumption structure. (The ratio of clothes and transportation expenditure over total household consumption is about 6% and 2.4%, respectively, in reference scenario, ranking, respectively, the fifth and sixth of the 19 household consumption goods.)

4.3 Impacts on employment

As shown in Table 3, the impacts of oil shortage on employment are negative. Figure 2 shows the variations of sectoral labor demand under different scenarios. Figure 2 shows that under all scenarios, labor demand decreases in almost all sectors except crude oil.
Fig. 2

Variations of sectoral labor demand under different scenarios

Sectors whose decreasing amplitude of labor demand surpass the sectoral average include petroleum processing, textile, clothing, electricity, equipment manufacturing, chemical, metalwork, paper, non-ferrous metal, other heavy industry, wood, food, and agriculture. Under all four scenarios, the greatest decreasing amplitude of labor demand corresponds to Petroleum Processing. Labor demand in this sector decreases by 1.28, 1.17, 0.08, and 0.68 percentage under scenario Ocris-1, Ocris-2, Ocris-3, and Ocris-M, respectively.

Under all scenarios, the four sectors whose employment reductions contribute greatest to national total employment reduction are subsequently agriculture, service, equipment manufacturing, and textile. Total employment reduction in these four sectors occupies about 77% to national total employment reduction under all scenarios.

4.4 Impacts on household welfare

Welfare reflects the contented state that households achieve through the consumption of various goods and services. Here the welfare index is employed to reflect the impact of oil shortage on household real purchasing power.

As shown in Table 3, both urban and rural welfare decrease under all oil shortage scenarios. This is because oil shortage will on the one hand increase domestic aggregate consumption goods price level, on the other hand lead to the decrease of household income.

Table 3 also shows that, under the same oil shortage scenario, welfare loss of rural households is greater than that of urban households. This is mainly because that, though the increasing rate of rural CPI is smaller than that of urban CPI under all scenarios, the decrease in rural households’ disposable income is greater than urban households. Moreover, for both urban and rural households, the fluctuation rates of CPI are much smaller than the corresponding fluctuation rates of disposable income. Therefore, the relationship between the variations of urban and rural welfare is consistent with the relationship between the variations of urban and rural household disposable income, i.e., rural welfare loss is greater than urban.

The main reason causing a greater decreasing rate of rural households’ disposable income than urban households under all scenarios is that: a large part of urban households’ income is from transfer payment, which is not affected by oil shortage and thus could cushion in a sense the decrease in total income. Given that currently the share of existing transfer income in total income of rural households is much smaller than that of urban households, rural households’ income is more sensitive to oil shortage, thus has a greater decreasing rate.

4.5 Impacts on production activity

4.5.1 Impacts on sectoral profit

Figure 3 shows the impacts of oil shortage on sectoral profit under different scenarios. Results show that, oil shortages cause profit loss in most sectors. Average sectoral profit loss under scenario Ocris-1, Ocris-2, Ocris-3, and Ocris-M is 0.10%, 0.09%, 0.01%, and 0.05%, respectively. Sectors whose decreasing rate of profit surpass the sectoral average under all scenarios include petroleum processing, textile, clothing, equipment manufacturing, electricity, other heavy industries, metalwork, paper, wood, food, non-ferrous metal, water, agriculture, service, iron and steel, chemical, and non-metal. Only crude oil sector experiences an increased profit under all scenarios.
Fig. 3

Variation of sectoral profit under different scenarios

4.5.2 Impacts on sectoral export

Figure 4 shows the impacts of oil shortage on sectoral export under different scenarios. Results show that, oil shortages lead to export reduction in almost all sectors except other heavy industries which experiences a fairly weak increase. Total export decreases by 0.30%, 0.27%, 0.02%, and 0.16%, respectively, under scenario Ocris-1, Ocris-2, Ocris-3, and Ocris-M.
Fig. 4

Variation of sectoral export under different scenarios

Sectors whose decreasing rate of export surpasses the sectoral average under all scenarios include petroleum processing, crude oil, transportation, chemical, electricity, textile, coking, and clothing. In particular, the decreasing rate of export in petroleum processing sector is much greater than other sectors. However, export of petroleum processing, electricity, crude oil, and coking are not holding important positions in the current export structure of China (the sum of these four sectors account for about 1% of the total export in the reference scenario), the export reduction of petroleum processing sector occupies a relatively high proportion at about 12% over national total export reduction, while the total exports reduction from the other three sectors occupies a fairly small ratio, about 1% under all scenarios.

Under all scenarios, the five sectors whose export reductions contribute greatest in national total export reduction are subsequently equipment manufacturing, chemistry, petroleum processing, textile, and transportation, which in total occupies about 77% of national total export reduction under each scenario.

5 Sensitivity analyses

5.1 Changes of world oil shortage rate

This section examines the robustness of model results by performing simulations under a broader range of world oil shortage rates, i.e., from 0.1 to 10% (step 0.5%). Results show that all the findings in Sect. 4 are robust to all of the tested oil shortage rates.

5.2 Changes of the key elasticity of substitution

Aware of the limitation of CGE models in setting different elastic parameters, and the energy trade efficiency promoting the cross-product substitution (Sheng et al. 2015), in this section sensitivity analyses are performed on some elasticities which are relatively critical to this study, as shown in Table 4. Here the simulations for different values of elasticities are carried out under the four oil shortage scenarios set in Sect. 3.
Table 4

Elastic parameters for sensitivity analysis and their values

Parameters

Current values

Downward adjustments

Upward adjustments

\(\sigma_{{{\text{M}} - {\text{D}}}}\)

4

− 75%

− 50%

− 25%

+ 25%

+ 50%

+ 75%

\(\sigma_{{{\text{K}} - {\text{E}}}}\)

0.9

− 75%

− 50%

− 25%

+ 25%

+ 50%

+ 75%

\(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\)

0.5

− 75%

− 50%

− 25%

+ 25%

+ 50%

+ 75%

\(\sigma_{{{\text{M}} - {\text{D}}}}\) means substitution elasticity between imported and domestic oil, \(\sigma_{{{\text{K}} - {\text{E}}}}\) means substitution elasticity between capital and energy, and \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\) means substitution elasticity between electricity and fossil fuels

5.2.1 Impacts on major macroeconomic indices given different elasticity values

As for the macroeconomic impacts, indices that are sensitive to the adjustments of the elasticity values include the extend of the impacts on total investment, total consumption, real GDP, as well as the ranking of the sectors which have great effect on raising national CPI.
  1. 1.

    Impacts on total investment, total consumption, and real GDP could become less negative, given a larger \(\sigma_{{{\text{M}} - {\text{D}}}}\), a smaller \(\sigma_{{{\text{K}} - {\text{E}}}}\), or smaller \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\). The decrease in enterprise savings could be less, given larger \(\sigma_{{{\text{M}} - {\text{D}}}}\), larger \(\sigma_{{{\text{K}} - {\text{E}}}}\), or smaller \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\). While, government savings could be increase further, given smaller \(\sigma_{{{\text{M}} - {\text{D}}}}\), smaller \(\sigma_{{{\text{K}} - {\text{E}}}}\), or smaller \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\).

     
  2. 2.

    As for the effects on raising national CPI, the prices decrease in gas production and supply, coal mining, and natural gas cannot offset the prices rise of the rest sectors when given larger \(\sigma_{{{\text{K}} - {\text{E}}}}\). Also, the prices rise of non-ferrous metal, paper, food, wood, metalwork, machinery, and water rank higher while the price rise of gas production and supply and natural gas rank lower when given larger \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\).

     

5.2.2 Impacts on major sector indices given different elasticity values

As for the impacts on sectors, indices that are sensitive to the adjustments of the elasticity values include the export, profit and employment.

When \(\sigma_{{{\text{M}} - {\text{D}}}}\) is smaller than the current value in CEEPA, the export of service good tend to be increase.

When \(\sigma_{{{\text{K}} - {\text{E}}}}\) is smaller than the current value in CEEPA, the profit of gas production and supply, coal mining, and natural gas will increase. The export of service will also increase. And the employment of gas production and supply, coal mining, natural gas, and electricity will increase, too. When \(\sigma_{{{\text{K}} - {\text{E}}}}\) is larger than the current value in CEEPA, the profit of chemical, construction, transportation, and coking good will increase under all scenarios. The export of other heavy industry and natural gas will also increase.

When \(\sigma_{{{\text{Ele}} - {\text{Fos}}}}\) is smaller than the current value in CEEPA, the employment of gas production and supply, and natural gas will increase.

6 Conclusions and future perspectives

This study employs CEEPA model to examine the possible impacts on major socioeconomic indices including economic growth, price level, employment, household welfare, and production activity under different oil shortage scenarios. Main conclusions of this study are as follows:
  • Oil shortage has negative impact on overall economic growth of China, and GDP loss will decrease obviously when the extent of shortage increases. Therefore, it is necessary to speed up the strategic petroleum reserve construction, especially given the current highly concentrated oil importing structure in China.

  • Given the dominant position of household consumption over total consumption, oil shortage will influence total consumption mainly through its impact on household consumption. As for the impacts on the two determinants of household consumption, oil shortage will raise CPI and reduce household disposable income. The impact of oil shortage on CPI is relatively weak. The decrease in household disposable income plays a much greater role in total consumption reduction than the increase in CPI.

  • The negative impact of oil shortage on household disposable income mainly comes from the loss of labor income incurred by the negative shock on employment. Therefore, when there is a shortage in oil supply, related measures should be implemented to address the decrease in employment, especially the employment in petroleum processing, textile, clothing, electricity, equipment manufacturing, chemical, metalwork, other heavy industry, paper, and non-ferrous metal.

  • The negative impacts of oil shortage on rural households’ disposable income and welfare are both greater than the corresponding urban values. That is, from the perspective of no matter income or expenditure, oil shortage will have greater negative impacts on the living standard of rural household than urban. Attention should be paid to the worsen income distribution status in this case.

  • Oil shortage has negative impacts on the profit of most sectors. Petroleum processing, textile, clothing, and equipment manufacturing sector are apt to more obvious negative impacts than the other sectors.

  • Oil shortage has negative impacts on the export of almost all sectors. Only in the case that domestic oil is relatively easily substituted for imported oil, or that the substitutability between capital and energy is relatively low, it is possible to slow down the decline in exports of most sectors. Petroleum processing, crude oil, transportation, chemical, and electricity are apt to more obvious negative impacts than the other sectors.

This study has just performed a preliminary analysis on the socioeconomic impacts of oil shortage in China. Further work is required to provide more effective decision support:
  1. 1.

    Improving discussions about the recycling manner of extra proceeds from increased domestic oil price: in this study these extra proceeds are assumed to be assigned to government budget as windfall profit tax. Results also show that such a way of treating extra proceeds is good for alleviating the negative impact of oil shortage on total investment thereby on overall economic growth. However, oil shortage will impact many aspects of the economic system, and there are many alternatives for revenue recycling. Whether there exists a kind of recycling manner, which has the best integrated effects on protecting overall economic growth, household life and production activity? This is the issue need to be further explored.

     
  2. 2.

    Analyzing from a global perspective: on the one hand, if there is an obvious shortage in international oil supply, usually international oil price will also change obviously. In such cases, including price factor could better describe the impacts of oil shortage, thus provide more effective decision supports. A global-scale model is required for more accurately simulating the variations of international oil price; on the other hand, oil shortage will not only influence China’s economy, but also impact economic development in other countries and therefore will impact the structure and distribution of world trade, especially impact the trade prices of various goods. This part of shocks on China’s economy, which is transferred through the impacts on global trade, is difficult to accurately simulate with a single-country model.

     

Notes

Acknowledgements

The authors gratefully acknowledge the financial support from the National Key Research and Development Program of China (2016YFA0602603), the National Natural Science Foundation of China (71422011 and 71461137006), and the China Scholarship Council (201706030103).

References

  1. Aydın L, Acar M (2011) Economic impact of oil price shocks on the Turkish economy in the coming decades: a dynamic CGE analysis. Energy Policy 39:1722–1731.  https://doi.org/10.1016/j.enpol.2010.12.051 CrossRefGoogle Scholar
  2. Bachmeier L (2008) Monetary policy and the transmission of oil shocks. J Macroecon 30:1738–1755.  https://doi.org/10.1016/j.jmacro.2007.11.002 CrossRefGoogle Scholar
  3. Bai Y, Zhou DQ, Zhou P (2012) Modelling and analysis of oil import tariff and stockpile policies for coping with supply disruptions. Appl Energy 97:84–90.  https://doi.org/10.1016/j.apenergy.2011.12.036 CrossRefGoogle Scholar
  4. Basher SA, Haug AA, Sadorsky P (2016) The impact of oil shocks on exchange rates: a Markov-switching approach. Energy Econ 54:11–23CrossRefGoogle Scholar
  5. BP (2008) BP statistical review of world energy. British Petroleum, LondonGoogle Scholar
  6. Broadstock DC, Wang R, Zhang D (2014) Direct and indirect oil shocks and their impacts upon energy related stocks. Econ Syst 38:451–467CrossRefGoogle Scholar
  7. Cha KS, Bae JH (2011) Dynamic impacts of high oil prices on the bioethanol and feedstock markets. Energy Policy 39:753–760.  https://doi.org/10.1016/j.enpol.2010.10.049 CrossRefGoogle Scholar
  8. CNPC (China National Petroleum Corporation) (2018) China’s oil dependency is close to 70%. http://center.cnpc.com.cn/bk/system/2018/03/14/001680985.shtml. Accessed 3 Jan 2018
  9. CSFB (China Society for Finance and Banking) (2008) Almanac of China’s Finance and Banking. Almanac of China’s Finance and Banking Editorial Board, BeijingGoogle Scholar
  10. Cuñado J, Pérez de Gracia F (2003) Do oil price shocks matter? Evidence for some European countries. Energy Econ 25:137–154.  https://doi.org/10.1016/S0140-9883(02)00099-3 CrossRefGoogle Scholar
  11. Du L, Yanan H, Wei C (2010) The relationship between oil price shocks and China’s macro-economy: an empirical analysis. Energy Policy 38:4142–4151.  https://doi.org/10.1016/j.enpol.2010.03.042 CrossRefGoogle Scholar
  12. Farzanegan MR, Markwardt G (2009) The effects of oil price shocks on the Iranian economy. Energy Econ 31:134–151CrossRefGoogle Scholar
  13. Fayyad A, Daly K (2011) The impact of oil price shocks on stock market returns: comparing GCC countries with the UK and USA. Emerg Mark Rev 12:61–78CrossRefGoogle Scholar
  14. Fu D (2008) Finance year book of China 2008. China Finance Magazine, BeijingGoogle Scholar
  15. GAC (General Administration of Customs) (2009) China customs statistics yearbook 2008. China Custom Magazine, BeijingGoogle Scholar
  16. Hirsch RL (2008) Mitigation of maximum world oil production: shortage scenarios. Energy Policy 36:881–889CrossRefGoogle Scholar
  17. IPCC (2006) IPCC guidelines for national greenhouse gas inventories. https://www.ipccnggip.iges.or.jp/public/2006gl/
  18. Jiménez-Rodríguez R (2015) Oil price shocks and stock markets: testing for non-linearity. Empir Econ 48:1079–1102CrossRefGoogle Scholar
  19. Kang W, Ratti RA, Yoon KH (2015) Time-varying effect of oil market shocks on the stock market. J Bank Financ 61:S150–S163CrossRefGoogle Scholar
  20. Korhonen I, Ledyaeva S (2010) Trade linkages and macroeconomic effects of the price of oil. Energy Econ 32:848–856CrossRefGoogle Scholar
  21. Lee BR, Lee K, Ratti RA (2001) Monetary policy, oil price shocks, and the Japanese economy. Jpn World Econ 13:321–349CrossRefGoogle Scholar
  22. Li Y, Shi X, Su B (2018) Economic, social and environmental impacts of fuel subsidies: a revisit of Malaysia. Energy Policy 110:51–61.  https://doi.org/10.1016/j.enpol.2017.08.015 CrossRefGoogle Scholar
  23. Liang Q-M, Wei Y-M (2012) Distributional impacts of taxing carbon in China: results from the CEEPA model. Appl Energy 92:545–551CrossRefGoogle Scholar
  24. Liang Q-M, Yao Y-F, Zhao L-T, Wang C, Yang R-G, Wei Y-M (2014) Platform for China energy environmental policy analysis: a general design and its application. Environ Model Softw 51:195–206.  https://doi.org/10.1016/j.envsoft.2013.09.032 CrossRefGoogle Scholar
  25. Lin S-M, Feng J-C, Ko F-K (2012) Assessing Taiwan’s energy security under climate change. Nat Hazards 62:3–15CrossRefGoogle Scholar
  26. NBS (National Bureau of Statistics) (2008) China statistical yearbook 2008. China Statistics Press, BeijingGoogle Scholar
  27. NBS (National Bureau of Statistics) (2009) Input–output table of China 2007. China Statistics Press, BeijingGoogle Scholar
  28. NBS (National Bureau of Statistics) (2010) China statistical yearbook 2010. China Statistics Press, BeijingGoogle Scholar
  29. Papapetrou E (2001) Oil price shocks, stock market, economic activity and employment in Greece. Energy Econ 23:511–532CrossRefGoogle Scholar
  30. Peersman G, Van Robays I (2012) Cross-country differences in the effects of oil shocks. Energy Econ 34:1532–1547CrossRefGoogle Scholar
  31. SAT (State Administration of Taxation) (2011) Query on export rebate rates. http://202.108.90.178/guoshui/action/InitChukou.do. Accessed 3 Jan 2018
  32. Scheitrum DP, Carter CA, Jaffe AM (2017) Testing substitution between private and public storage in the U.S. oil market: a study on the U.S. strategic petroleum reserve. Energy Econ 64:483–493.  https://doi.org/10.1016/j.eneco.2015.10.015 CrossRefGoogle Scholar
  33. Segal P (2007) Why do oil price shocks no longer shock? Oxford Institute for Energy Studies, Oxford. https://ora.ox.ac.uk/objects/uuid:ca9ccad3-2814-4f57-8a80-6a21367db214
  34. Sheng Y, Wu Y, Shi X, Zhang D (2015) Energy trade efficiency and its determinants: a Malmquist index approach. Energy Econ 50:306–314.  https://doi.org/10.1016/j.eneco.2015.05.019 CrossRefGoogle Scholar
  35. Shi X, Sun S (2017) Energy price, regulatory price distortion and economic growth: a case study of China. Energy Econ 63:261–271.  https://doi.org/10.1016/j.eneco.2017.02.006 CrossRefGoogle Scholar
  36. Stern DI (1993) Energy and economic growth in the USA: a multivariate approach. Energy Econ 15:137–150CrossRefGoogle Scholar
  37. Sun M, Gao C, Shen B (2014) Quantifying China’s oil import risks and the impact on the national economy. Energy Policy 67:605–611.  https://doi.org/10.1016/j.enpol.2013.12.061 CrossRefGoogle Scholar
  38. Tverberg GE (2012) Oil supply limits and the continuing financial crisis. Energy 37:27–34CrossRefGoogle Scholar
  39. Vásconez VA, Giraud G, Mc Isaac F, Pham N-S (2015) The effects of oil price shocks in a new-Keynesian framework with capital accumulation. Energy Policy 86:844–854CrossRefGoogle Scholar
  40. Walras L (1969) Elements of pure economics: or the theory of social wealth. AM Kelly, New YorkGoogle Scholar
  41. Wang C (2003) Climate change policy simulation and uncertainty analysis: a dynamic CGE model of China. Tsinghua University, BeijingGoogle Scholar
  42. Wei Y, Wu G, Liang Q, Liao H (2012) China energy report (2012): energy security research. Science Press, BeijingGoogle Scholar
  43. Wu Y, Xuan X (2002) The economic theory of environmental tax and its application in China. Economic Science Press, BeijingGoogle Scholar
  44. Wu G, Wei Y-M, Nielsen C, Lu X, McElroy MB (2012) A dynamic programming model of China’s strategic petroleum reserve: general strategy and the effect of emergencies. Energy Econ 34:1234–1243CrossRefGoogle Scholar
  45. Xue X (1998) Calculation and comparison study of CO2 emission from China’s energy consumption. Environ Prot 4:27–28Google Scholar
  46. Yeoman I, Lennon JJ, Blake A, Galt M, Greenwood C, McMahon-Beattie U (2007) Oil depletion: what does this mean for Scottish tourism? Tour Manag 28:1354–1365CrossRefGoogle Scholar
  47. Zhang B, Li X-M (2016) Recent hikes in oil-equity market correlations: transitory or permanent? Energy Econ 53:305–315CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Center for Energy and Environmental Policy Research, Beijing Institute of TechnologyBeijingChina
  2. 2.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina
  3. 3.Collaborative Innovation Center of Electric Vehicles in BeijingBeijingChina
  4. 4.National Natural Science Foundation of ChinaBeijingChina
  5. 5.China Development InstituteShenzhenChina
  6. 6.Sinopec Research Institute of Petroleum EngineeringBeijingChina

Personalised recommendations