Impacts of exchange rate volatility and international oil price shock on China's regional economy: A dynamic CGE analysis

    Impacts of Exchange Rate Volatility and International Oil Price Shock on China’s Regional Economy: A Dynamic CGE Analysis Baomin Dong, Xili Ma, Ningjing Wang, Weixian Wei PII: DOI: Reference:

S0140-9883(17)30317-1 doi: 10.1016/j.eneco.2017.09.014 ENEECO 3762

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

21 April 2017 16 August 2017 18 September 2017

Please cite this article as: Dong, Baomin, Ma, Xili, Wang, Ningjing, Wei, Weixian, Impacts of Exchange Rate Volatility and International Oil Price Shock on China’s Regional Economy: A Dynamic CGE Analysis, Energy Economics (2017), doi: 10.1016/j.eneco.2017.09.014

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The Impacts of Exchange Rate Volatility and International Oil Price Shock on China’s Regional Economy: A Dynamic CGE Analysis

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Baomin Dong∗, Xili Ma†, Ningjing Wang‡, Weixian Wei§

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August 16, 2017

∗ School of Economics, Henan University, 1 Jinming Road, Kaifeng, Henan, China 475000. Email: [email protected] † School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China ‡ School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China § Corresponding author. School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China. Email: [email protected]

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Impacts of Exchange Rate Volatility and International Oil Price Shock on China’s Regional Economy: A Dynamic CGE Analysis

Abstract

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A multi-regional dynamic computable general equilibrium model is constructed in this paper to explore the macroeconomic effects of international oil price shocks and RMB exchange rate changes on China. The results show that (1) in terms of regional development differences, the decrease in international oil prices and depreciation of RMB are both conducive to economic growth, although the impact of RMB devaluation is more obvious. Increases in international oil prices will further widen the output gap between the rich and the poor regions, whereas oil price decreases and RMB devaluation will narrow the regional development differences. (2) In terms of employment, the depreciation of the exchange rate and the decline in international oil prices will help increase the employment rate in most regions, but oil price hikes will be most beneficial for improving oil industry employment in the northeast. (3) The impact of oil price volatility is asymmetric. Compared with rising oil prices, falling oil prices have significantly greater effects on GDP, industrial output, employment and other aspects. Furthermore, the impacts of exchange rate fluctuations and oil price changes on the regional economy exhibit a time lag. Keywords: international oil price, RMB exchange rate, China regional economy, dynamic regional CGE Model 1. Introduction On August 11, 2015, the RMB depreciated 2% in one day against the US$. By August 2016, the one-year accumulative depreciation of RMB against the US$ was 8% and 30% against the Japanese Yen. China spent 500 billion US$ to stabilize the RMB exchange rate in 2016, lowering China’s foreign reserve

Preprint submitted to Energy Economics

August 16, 2017

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to $3.3 trillion by the end of 2016 1 . In October 2016, the RMB officially joined the SDR currency basket, marking the recognition on enormous change in China in the past decade that had made the yuan more open. However, analyses fear that the inclusion of RMB to the SDR would make China to allow renewed depreciation. On December 15, 2016, the Federal Reserve announced that the federal fund’s rate would increase by 25 basis points, and it indicated that interest may be raised three times in 2017. Rising US interest rates further strengthened the expectation of RMB depreciation and made short-term RMB depreciation inevitable (e.g., Zhang and Zhang, 2017). At the same time, the Chinese macroeconomy is also not immune to the fluctuations of world crude oil prices as over 60% of China’s oil consumption relies on import 2 . According to International Energy Agency’s (IEA) 2016 World Energy Outlook (WEO) 3 , under the Current Policies Scenario (CPS), even without an oil price shock the global price of crude oil is projected to rise to $82 per barrel (bbl) by 2020, $127/bbl by 2030, and $146/bbl by 2040. Under the New Policies Scenario (NPS) which assumes that governments pledge to Paris Agreement on climate change, oil prices are projected to rise to $79/barrel by 2020, $111/bbl by 2030, and $124/bbl by 2040. This is because world oil demand grows fairly steadily over the past 30 years to 92.5 million barrels per day (bpd) in 2015, or 35 million bpd increase. Thus the CPS extrapolation estimates oil demand at 117 million bpd by 2040 even with the consideration of the development of biofuels and electric vehicles. The IEA report also warns about the potential for a near-term oil price shock as investment falls short of what is needed to keep up with growing demand. The IEA report projects that oil price to increase 75% in the next three years under conservative assumptions and even more in bullish forecasts. Fluctuations in exchange rate and oil prices have direct impacts on the output and price level of the economy, they affect intra/intertemporal consumption decisions, and also influence the input substitution and cost 1

Charles Riley, China spent US$ 500 billion to prop up the Yuan 2016, CNN, Jan 7, 2016 http://money.cnn.com/2016/01/07/investing/china-foreign-reserves-yuancurrency/index.html. 2 Source: National Bureau of Statistics of China, http://data.stats.gov.cn/publish.htm?sort=1. 3 Source: the US Energy Information Administration, http://www.iea.org/bookshop/720-World Energy Outlook 2016.

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2. Literature review

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structure of firms, although some impacts are channeled through a secondary effect on domestic prices. What are the impacts of an oil price shock and/or exchange rate shock on regional outputs across China? How are they going to affect inflation rate movement? To shed some light on these, we quantify the impact of an oil price shock on output and inflation using a multiregional dynamic CGE model in this paper. In particular, we allow flexible elasticity of substitution between oil and other types of consumption goods in the consumption bundle, and also in the technology used by domestic firms. The paper is organized as follows: Section 2 provides a literature review; Section 3 describes the CGE model developed for the study; Section 4 provides the data processing, scenario setting, and model evaluation; Section 5 presents the simulation results; and the conclusions and discussion are provided in the last.

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Hamilton (1983) was one of the first to address the economic impacts of oil price in which he argues that the sharp rise in oil prices between 1948 and 1972 was a central factor contributing to the US recession, rising inflation, and high unemployment. In a recent survey on studies of oil price fluctuations between 1973 and 2014, Baumeister and Kilian (2016) find that despite a large literature on the economic determinants of oil price fluctuations on this topic and improved understanding, oil price fluctuations remain to be difficult to predict. Studies on the impacts of oil price shocks on China’s macroeconomy were rare until now. For instance, using an open economy DSGE model, Zhao et al. (2016) consider four types of sources for oil price shocks, i.e., political events, oil supply shock, aggregate demand shock for industrial commodities, and demand shocks specific to crude oil market, and show that political driven oil price shocks produce short run effects on China’s output and inflation. However, due to the representative household assumption, the model is unable to study the regional difference in response to oil price shocks. Several studies investigated the empirical relationship between oil price and Chinese economy. Using 1995 to 2008 time series data estimated in a VAR model, Du et al. (2010) find that although inflation was pushed up by oil price hike, oil price downturns retarded Chinese economic growth more than the hikes did. The asymmetric impacts of oil price on output are striking since it is commonly believed that an upward oil price shock would impair the 3

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economy. Since it was a country wide empirical study which may be subject to uncontrolled confounders, the paper was unable to offer a clear-cut interpretation although the authors speculate that the fall of oil price may have been the consequence of depression in advanced economies (thus negative to Chinese economy) whereas oil price hikes were supply shocks that were uniform to all counties. The other conclusion drawn by this study that Chinese economy does not affect world oil price, justifies our exogenous oil price shock assumption in the current paper. In another recent paper, Cross and Nguyen (2017) use a time-varying VAR model to show that global oil price shocks on China’s output is often small and temporary and China’s output shocks have no significant impact on global oil market, thus also supporting the unidirectional causality run from international oil price to Chinese economic aggregates. On top of the time-varying effect of oil price on Chinese economy, Nguyen and Cross (2017) also show that, in contrast to the US economy, the monetary policy in China in response to world energy price shocks is found to be more pro GDP growth rather than inflation stabilization. In the current paper, we postulate different degrees of policy response to oil price shocks to quantify the impacts. Studies using CGE models to investigate the economic impact of exchange rate in China have been also recently developed (e.g., Yu et al., 2003; Willenbockel, 2006; Tyers and Yang, 2000; Yang et al., 2013; Li and Xu, 2011; Meng, 2015). However the focuses and approaches differ from the current paper. For instance, Yu et al. (2003) mainly consider the substitution effect between export rebate and RMB devaluation. Tyers and Yang (2000) find similar impacts on the economy between nominal wage increase and nominal exchange rate devaluation. Yang et al. (2013) study the short-term impact of RMB appreciation on China’s bilateral trade with trade partners. Willenbockel (2006) assumes that the real exchange rate shock is realized by a decrease in Chinese saving rate. Li and Xu (2011) compare the effectiveness of real appreciation led by increased wage rate and decreased gross national saving rate on China’s trade balance and terms of trade. Meng (2015) studies the impact of RMB appreciation by constructing a multi-currency GTAP model to allow bilateral exchange rates for any two regions. However, the received literatures do not study the impacts of RMB depreciation in subnational scale using a dynamic model. The current paper therefore fills this gap. The statistical association between oil price and currency depreciation is subject to debate. Existing studies find all three possible associations, i.e., 4

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positive association (e.g., Cifarelli and Paladino (2010) for dollars); negative association; and no association (e.g., Huang and Guo (2007) on China; Hussain et al. (2017) on Asian countries). In light of these, we assume that oil price and RMB devaluation movements are exogenously determined, and we focus on the macroeconomic consequence of the two shocks in different combinations implied by the empirical literature. This current study contributes to this growing literature in the following aspects. (1) A dynamic CGE model is built for the study because previous CGE models for this problem (Liu et al., 2015; Meng, 2015) are almost all static. For example, Liu et al. (2015) simulated scenarios in which international oil prices rose by 100% with a static CGE model. However, the impacts of oil price changes and exchange rate fluctuations on the industry output and macroeconomy are not always reflected in the current period, and when policymakers would like to identify long-term effects, a dynamic CGE model performs better in this sense; (2) The multi-region nature of our model enables us to address regional responses to the shocks in contrast to the existing literature (Du et al., 2010; Jeanneney and Hua, 2011; Huang and Guo, 2007; Timilsina, 2015). The introduction of multi-region setting is important since the regional disparities in China are wide. The multi-region model is also able to incorporate domestic trade among different regions influenced by oil prices and exchange rates fluctuations; (3) This paper explores the impacts of both international oil price volatility and RMB exchange rate changes. Many studies have researched the effect of oil price changes (Becken and Lennox, 2012; Cavalcanti and Jalles, 2013; Cifarelli and Paladino, 2010), the economic shocks caused by exchange rate fluctuations (Carrera and Vergara, 2012; Faleiros et al., 2016; Feng and Alon, 2007), whereas few studies have analyzed simultaneous changes of the two variables. (4) Energy substitutions upon oil price shocks is studied. The current paper studies energy substitution by decomposing the energy sector into coal, oil, natural gas and electricity generation and constructing a multi-level nested function. 3. Dynamic multi-regional CGE model The CGE model established in this paper includes modules such as production, consumption, dynamic recursion, imports and exports, and price indexes. The schematic structure of the CGE model is shown in Fig.1. The following is a brief description of the main modules.

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3.1. Production Fig.2 presents the production process, which is composed of nine layers. At the top nest, the total output consists of basic inputs and production taxes. Because the production taxes imposed by the government increase only a firm’s production costs without affecting the output, alternative possibilities between the taxes and costs are not available, and they are represented by the Leontief function. Eq.(1) indicates that industrial output is equal to basic input. Eq.(2) describes the relationship between pre-tax base prices and post-tax production prices. We assume that production tax rates remain constant during the simulation period, which does not have the period subscript t. QOU Ti,r,t = QBASi,r,t P OU Ti,r,t = (1 + tproi,r )P BASi,r,t

(1) (2)

where QOU Ti,r,t is the total output of sector i in region r, and in period t 4 ; QBASi,r,t is the basic input of the composite quantity; P OU Ti,r,t and 4

Subscript (i,r,t) represent industry, region, and period, respectively. In the absence

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P BASi,r,t are the prices of output and input, respectively; and tproi,r is the production tax rate. In the second tier, the basic inputs are divided into two parts nested by the Leontief function. The first part is energy-capital-labor bundle, and the second part is the bundle of intermediate inputs. Eq.(3) represents the quantitative relationship between energy-capital-labor bundle and the basic inputs. Similarly, Eq.(4) represents the quantitative relationship between the bundles of intermediate inputs and the basic inputs. Eq.(5) is the value (quantity multiplied by the price) identity; that is, the amount of basic investments is equal to the sum of the value of energy-capital-labor bundles and the value of intermediate inputs.

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QEKLi,r,t = aekli,r,t QBASi,r,t QIN Ti,r,t = (1 − aekli,r,t )QBASi,r,t P BASi,r,t QBASi,r,t = P IN Ti,r,t QIN Ti,r,t + P EKLi,r,t QEKLi,r,t

(3) (4) (5)

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where QEKLi,r,t represents the energy-capital-labor bundles; QIN Ti,r,t is the compound intermediate inputs; P EKLi,r,t and P IN Ti,r,t are the corresponding prices, respectively; and aekli,r,t is the share of energy-capital-labor bundles in basic inputs. In the third tier of energy-capital-labor compounds, energy-capital compound and labor are aggregated through the constant elasticity of substitution (CES) function following Zhang et al. (2013). Eq.(6) is a CES production function; Eq.(7) is the value identity; and Eq.(8) is derived from the theory of production cost minimization, which states that the input ratio of energy-capital compound to labor is affected by the price ratio and other parameters. h −ρekl QEKLi,r,t = BEKLi,r,t βeki,r,t QEKi,r,t i,r,t +

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(1 − βeki,r,t )QLi,r,t

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P EKLi,r,t QEKLi,r,t = P EKi,r,t QEKi,r,t +P Li,r,t QLi,r,t  σekli,r,t P Li,r,t βeki,r,t QEKi,r,t = QLi,r,t 1 − βeki,r,t P EKi,r,t of ambiguity, similar explanations are omitted below.

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where BEKLi,r,t is the size parameter; QEKi,r,t and P EKi,r,t are the quantity and price of energy-capital compounds, respectively; QLi,r,t and P Li,r,t are the quantity and price of labor, respectively; βeki,r,t is the share parameter of energy-capital compound products; σekli,r,t is the elasticity of substitution of energy-capital compound products and labor, and the parameter ρekli,r,t = (1 − σekli,r,t ) /σekli,r,t . In the third tier of intermediate inputs, compound intermediate inputs are composed of N commodities through the Leontief function. The equation expressions are similar to Eqs.(3)–(5) and hence are omitted here. In the fourth tier, energy-capital compound products are a CES composite of energy and capital. The corresponding equation is similar to Eqs.(6)–(8) and are omitted here. In the fifth tier, energy is divided into fossil fuels and electric energy, followed by fossil fuels divided into coal and non-coal in the sixth tier. In the seventh tier, non-coal fossil energy is composed of oil and gas. Each type of fossil energy and of intermediate inputs is composed of domestic and imported parts nested by CES function (the eighth tier of oil and natural gas; the seventh tier of coal; the sixth tier of electricity; and the fourth tier of intermediate inputs). Among them, domestic goods are nested by goods from multiple regions. In the case of oil, Eq.(9)–(11) depict how domestic oil consumption by industry i in region r is aggregated.

Q7DOILi,r,t = B7DOILi,r,t

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β7doils,i,r,t Q7DSOILs,i,r,t

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β7doils,i,r,t P 7DOILi,r,t P 7DSOILs,i,r,t

i,r,t

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P 7DSOILs,i,r,t Q7DSOILs,i,r,t

s

Q7DOILi,r,t

(11)

where Q7DOILi,r,t and P 7DOILi,r,t are the quantity and price of total domestic oil demand of industry i in region r, respectively; Q7DSOILs,i,r,t and P 7DSOILs,i,r,t are the quantity and price of oil purchased by industry 9

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i in region r from region s. B7DOILi,r,t is the scale parameter of the CES function; σ7doili,r,t represents the oil demand elasticity; and the parameter ρ7doili,r,t = (1 − σ7doili,r,t ) /σ7doili,r,t .

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3.2. Interregional trade Interregional trade is characterized by the interregional export and import module and highlights a main contribution of this study. Recall in Fig.1, firm i in region r can export its own products to foreign countries as well as regions within the country. We use a constant elasticity transfer (CET) function (Eqs.(12)–(14)) following Ochuodho et al. (2016). The aggregate sales to all regions is governed by Eq.(12). Eq.(13) depicts the sales to region r. Eq.(14) is the composite price of industry i.

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(B1Di,s,t )1+σ1di,s,t P P 1DRs,i,r,t Q1DRs,i,r,t s P 1Di,s,t = Q1Di,s,t

i,s,t

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(12) (13)

(14)

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where Q1Di,s,t is the aggregate quantity sold domestically, P 1Di,s,t is the domestic composite price; Q1DRs,i,r,t represents the sales volume by region s to region r ; P 1DRs,i,r,t is the corresponding price, and β1drs,i,r,t is the share of firm i’s output sold to region r against total domestic sales; B1Di,s,t is the scale factor of the CET function; σ1di,s,t is the elasticity coefficient of the CET function; and the intermediate parameter ρ1di,s,t satisfies: ρ1di,s,t =

1+σ1di,s,t σ1di,s,t

The interregional import module can be constructed in a similar fashion and omitted here. 3.3. International trade Unlike traditional national CGE models (Du et al., 2010; Jeanneney and Hua, 2011) the current model assumes that each region trades directly in 10

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the foreign market. The ratio of exports to domestic sales is governed by the Armington condition (Armington, 1969) shown by Eq.(15) where the ratio of export to domestic sales is determined by the corresponding price ratio. Eq.(16) depicts the relationship between export price expressed in RMB and US dollars with export tax (-rebate) adjustment. Eq.(17) shows how international market demand respond to nominal price, exchange rates and the demand elasticity. σexi,s,t 1 − β1exi,s,t P Wi,s,t Q1EXi,s,t = Q1Di,s,t β1exi,s,t P 1Di,s,t P Wi,s,t = P W Xi,s,t · Ednor,t  σwdi,t P W ORLDi,t · Ednor,t Q1EXi,s,t =Q1EX0i,s,t P Wi,s,t

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(15) (16) (17)

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where Q1EXi,s,t and Q1Di,s,t are the volumes of domestic sales and export abroad; P 1Di,s,t is the price in domestic sales; P Wi,s,t is the pre-tax export price in RMB; P W Xi,s,t is the foreign price after tax in US dollar; Ednor,t is nominal RMB exchange rate in period t in direct quotation, which indicates that 1 US dollar = Ednor,t RMB; P W ORLDi,t is the international market price of goods i ; Q1EX0i,s,t is the international market demand in the baseline scenario; and σwdi,t is the demand elasticity of the international market. The import sectors can be similarly defined. 3.4. Exchange rate In CGE models, only real exchange rates matter. The real exchange rates are converted from nominal exchange rates. Eq.(18)–(21) characterize the calculations of the real exchange rates. Eq.(18) follows the principle of the Fisher Index to calculate the GDP deflator in region i. Eq.(19) further calculates the GDP deflator at the national level, which is the weighted average of deflators in all regions. Eq.(20) defines the RMB real exchange rate whereas Eq.(21) is the inverse of real RMB exchange rate.

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vP P u i,r,t O u (P V Ai,r,t + TVIP )V A P V Ai,r,t V Ai,r,t + T IPi,r,t i,r A i,r,t u i i u =t P O P O O T IPi,r P V AO i,r V Ai,r + T IPi,r )V A (P V AO + i

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GDP Ct P IXGDP U SAt Edreal,t = Ednor,t P IXGDP Ct Eindreal,t = 1/Edreal,t

(19) (20) (21)

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where P V Ai,r,t and V Ai,r,t are the price and quantity of sectoral added value, respectively; T IPi,r,t is production tax; P IXGDP Cr,t is the GDP deflator of region r ; P 1XGDP Ct is the GDP deflator of China; GDP Ct is China’s total GDP; P IXGDP U SAt is the price level in the USA; Ednor,t is the nominal exchange rate represented in direct quotation; and Eindreal,t and Edreal,t are the real exchange rates expressed in dollars against RMB and the reverse, respectively.

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3.5. Dynamics This paper uses a recursive method as in the majority of CGE models (Hbler, 2011; Li and Lu, 2015; Xiao et al., 2015; Zhang, 1998). Moreover, we build a multi-region dynamic recursive mechanism based on the single region national model introduced by Horridge (2002). Related to investment, the most crucial variable in recursive dynamics is capital stock, and the amount of investment depends on investors’ judgment of certain industries. Eq.(22) calculates the M ratio, the ratio of the expected rate of return to long-term steady-state return rate for a certain sector. Investors’ willingness to invest changes in the same direction as the M ratio. Eq.(23) presents the current gross growth rate based on the M ratio and the industry’s long-term stable growth rate. Eq.(24) calculates the investment for an industry in the current period. Eq.(25) represents the relationship between capital stock and the amount of investment between two adjacent periods. Eq.(26) defines the actual rate of return in the current period. Eq.(27) is the process by which investors adjust their expectation over the returns for the next period based on the current real return rate. 12

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(22)

Ii,r,t =Gi,r,t Ki,r,t Ki,r,t+1 = Ii,r,t + (1 − Depi,r,t )Ki,r,t P Ki,r,t Ri,r,t = COi,r,t (1 + IRi,t ) Ei,r,t = (1 − b) Ei,r,t−1 + bRi,r,t

(24) (25)

(23)

(26) (27)

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Mi,r,t = Ei,r,t /Rnormal,i,r a Q ∗ Gi,r,trend ∗ Mi,r,t Gi,r,t = a Q − 1 + Mi,r,t

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where Ei,r,t is an investor’s rational expectation of the industry’s investment return for period t in period t-1 ; Rnormal,i,r is the industry’s long-term normal rate of return on investment; Mi,r,t is the M ratio; Gi,r,t is the current gross growth rate of capital stock; Gi,r,trend is the long-term steady-state growth rate; P Ki,r,t and COi,r,t are the capital rental price and the user cost, respectively; IRi,t is the interest rate; Ii,r,t and Ki,r,t are the amount of investment and capital stock, respectively; Depi,r,t represents the depreciation rate of capital; Q, a and b are exogenous coefficients.

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3.6. Other modules In addition to the above modules, the CGE model also includes government revenue and expenditure, household income and consumption, macroeconomic accounting, model closure and other modules. The government revenue is collected through production tax, personal income tax, tariffs and so on. The government expenditure mainly consists of direct purchases and savings. Government savings can be negative, or fiscal deficit. Household income mainly comes from labor wages and capital income. Household spending includes personal income tax, savings and consumption. The utility function of households is set following Geary (1950). Model closure imposes process of setting certain indicators as exogenous variables to make the model solvable (Verikios and Zhang, 2015). In this paper, the RMB nominal exchange rate is specified as an exogenous variable to explore the impact of exchange rate changes on domestic price and other variables. The international product prices are assumed exogenous as well. A variety of taxes, including production tax, customs duties and personal income tax, are all exogenous. The wage rate is supposed as a numeraire. 13

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4. Data processing, scenario setting, and model evaluation

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4.1. Data and regions The multi-regional input–output table is an indispensable data source for this study. Liu et al. (2014) provide the latest (2010) regional IO tables at province level. At a geographical scope, we merge thirty provincial administrative regions into four (see Table 1 and Fig.3) following He et al. (2017) and Salike (2016). Table 1: Definition of Regions Provinces

Northeast East Central West

Heilongjiang, Jilin, Liaoning Beijing,Tianjin,Hebei,Shanghai,Jiangsu,Zhejiang,Fujian,Shandong,Guangdong,Hainan Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan Ningxia,Gansu,Qinghai,Xinjiang,Sichuan,Chongqing,Guangxi,Yunnan,Guizhou,Inner Mongolia, Shaanxi

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Region

Northeast East

Central West

Fig. 3: Geographical Illustration of the Four Regions of China

For industries, to explore the impact of oil price changes on other energy sources, we divide the “oil and gas” sector into two sectors: an oil sector and a gas sector, and the coal and electricity sectors are retained. Apart from the energy sector, we categorize other sectors into the agriculture, manufacture, construction, and service sectors (see Table A.1). The USD/CNY exchange rate data is from Guotaian financial and economic database; international oil prices are represented by WTI crude oil futures prices from the US Energy Information Administration. Table A.2 lists the 2015 values of GDP, per capita GDP, import and export trade in the four regions.

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4.2. Scenario setting In the dynamic CGE model, Business-As-Usual (BAU) refers to the case without ad hoc policy interventions, which serves as the benchmark and reference for subsequent simulations. The setting of BAU includes the assignment of key variables, such as economic growth, labor force growth and so on. The GDP growth rate projection follows the IEA’s predictions (Agency, 2015). The energy intensity in China has been trending down and is expected to decline further. Cheng et al. (2016) assume that China’s annual energy efficiency growth rate ranges from 2% to 6% by 2020; Fujimori et al. (2014) assume this rate to be 1%–3% by 2050. Thus we assume that energy efficiency will continue to improve. The main variables set in BAU are provided in detail in Table 2. Table 2: Specific settings of BAU Labor force increasing rate (%)

Energy efficiency (%)

6.33 6.33 6.33 6.33 5.76 5.76 5.76 5.76 5.76 5.49 5.49 5.49 5.49 5.49

0.39 0.39 0.39 0.39 0.17 0.17 0.17 0.17 0.17 0.01 0.01 0.01 0.01 0.01

2.5 2.5 2.5 2.5 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

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2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

GDP increasing rate (%)

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The US Energy Information Administration (EIA) has made three predictions of WTI oil prices, including projections in BAU and two other scenarios, which would provide references in our scenario setting. Among the scenarios, BAU corresponds to the EIA baseline scenario, scenario A01 corresponds to the EIA’s high oil price scenario and scenario A02 corresponds to the low oil price scenario5 . The RMB has been depreciating against the U.S. dollar in recent years. In light of Yu6 , the upper bound for this depreciation would be 25% by 2023 5

Details of the annual oil price projections in http://tonto.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RCLC1&f=D. 6 Source: Dong Ding, Yongding Yu: RMB may depreciate against the dollars by as much as 25%, HuiTong.com.cn, http://news.fx678.com/C/20170109/201701091101322276.shtml.

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relative to 2016 level (see Table 3). Nevertheless, forecasts show that the size of Chinese economy, measured by GDP in PPP(purchasing power parity) terms, would surpass US by 2030 (e.g., PwC, 2017). It is then assumed that the exchange rate will regress back to 2016 level by 2030. This paper quantifies the impacts at regional level for the aforementioned exchange rate scenario.

0.1442 0.1384 0.1328 0.1274 0.1223 0.1174 0.1127 0.1081 0.1127 0.1174 0.1223 0.1274 0.1328 0.1384 0.1442

Year-on-year RMB’s value change (%)

-4.0264 -7.8908 -11.5995 -15.1589 -18.5749 -21.8535 -25.0000 -21.8535 -18.5749 -15.1589 -11.5995 -7.8908 -4.0264 0.0000

-4.0264 -4.0264 -4.0264 -4.0264 -4.0264 -4.0264 -4.0264 4.1954 4.1954 4.1954 4.1954 4.1954 4.1954 4.1954

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RMB’s value change from 2016(%)

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USD/CNY

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Table 3: Setting the change path in RMB’s value

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The out-of-sample forecasting of exchange rates is always a challenging work in academics (e.g., Sarno and Taylor, 2002). In a landmark paper, Meese and Rogoff (1983) compare the out-of-sample forecasts produced by various exchange rate models with forecasts produced by a random walk model. Their conclusion is that, on a comparison of root mean square errors (RMSEs), none of the asset-market exchange rate models outperforms the simple random walk. However, using a simple PPP measure of fundamentals for several major US dollar exchange rates during the recent float, Kilian and Taylor (2003) find strong evidence of long-horizon predictability of the nominal exchange rate. This empirical evidence also support we adopt Yu’s view on the long-term trend of the RMB’s value. In addition to the single scenario above, we also set two composite scenarios. Scenario A11 is a composite scenario of A01 and A03; accordingly, scenario A12 is a combination of A02 and A03. We perform a Pearson correlation test for the change rates between RMB exchange rate and international crude oil price for various frequencies (daily, weekly, monthly, quarterly) from 4 January 2007 to 14 April 20177 . The results indicate that there is no sig7

Source: RMB exchange rate is from Guotaian financial and economic database,

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nificant correlation between the two variables. This is congruent with the findings in the existing literature (e.g., Hussain et al., 2017; Huang and Guo, 2007). This justifies scenarios A11 and A12.

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4.3. Model Evalution The applicability and rationality of the regional dynamic CGE model built is the primary problem to confirm. Various measures have been proposed for assessing the predictive accuracy of forecasting models. 8 Most of these measures are designed to evaluate ex post forecasts, that is, the typical model evaluation is done retrospectively rather than in real time (e.g., West, 2006). A classic example is Meese and Rogoff (1983) evaluation for exchange rate models. Four measures are used in this paper to evaluate the effect of intra prediction of the CGE model, they are the Root Mean Square error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE) and the Theil Inequality Coefficient (TIC). Since the actual values of macro variables in 2016 is not yet available, our forecast range is from 2011 to 2015. Both international oil price fluctuations and RMB exchange rate oscillations have significant impacts on international trade, thus we use regional imports and exports as our forecast variables. Table A.3 (in Appendix A) lists the calculation results. Most divergences between predicted and true values are as small as acceptable, the RMSEs are all less than 0.25 and the MAEs are all less than 0.3. Because RMSE and MAE are probably affected by the magnitude of data, we further use MAPE and TIC which could standardize the errors, making the data less affected by magnitude. It can be seen that the MAPEs of regional imports and exports are all below 7%, and the TICs are all less than 0.02. In general, each variable’s MAPEs and TICs are sufficiently low, indicating that the accuracy of model forecasting is within the acceptable range. http://www.gtarsc.com/. International oil price is from the US Energy Information Administration, http://www.eia.gov/. 8 see Greene (2003).

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5. Simulation results

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5.1. Impact on regional development differences In the high oil price scenario (A01), the regional outputs are slightly lower than that in the baseline (see Tables 4 and 5). Among the four regions, the economic slowdown in the northeast is the most obvious because of rising oil prices, whereas it is much smaller in the central region. The differences in the impact of increasing international oil prices on the regional economy are mainly determined by the industrial structure, i.e., the proportion of oil production in the regional industrial output (see Table A.4 in Appendix A). In the basic data, the value of oil output accounts for 6.39% of the total industry in the northeast but only 0.2% in the central region. The role of oil industry in the northeast economy is naturally greater than that in the central region. Overall, The impact of oil price fluctuations is not overwhelming significant on the national economy, which is similar to the findings of Cross and Nguyen (2017).

A01

A02

A03

A11

A12

Central East Northeast West

-0.10 -0.17 -0.35 -0.28

0.31 0.58 1.43 1.08

1.91 1.69 1.56 1.77

1.77 1.49 1.18 1.46

2.29 2.34 3.05 2.92

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Table 4: Changes in GDP by region from BAU in 2023 (%)

Table 5: Changes in GDP by region from BAU in 2030 (%)

Central East Northeast West

A01

A02

A03

A11

A12

-0.09 -0.16 -0.31 -0.25

0.28 0.55 1.13 0.93

0.17 0.09 0.19 0.15

0.08 -0.05 -0.08 -0.07

0.44 0.58 1.16 0.98

In the A03 scenario, the RMB depreciates and the total economic output of each region is simulated by RMB devaluation before 2023. Compared with 2016, a 25% accumulative RMB depreciation at the end of 2023 increases the regional GDP by 1.5–1.9% relative to the baseline. Among the regions, the central region benefits the most, and the northeast benefits the least. In scenario A11, the international oil price increases countervail the RMB depreciation on the economy, but the former is insufficient to offset the latter under our assumptions. Thus, the net effect is a moderate increase of GDP in all regions. In this scenario, the economic decline in the four regions is 18

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less than that in the A01 scenario in 2030. From the differences between A11 and A01, although the magnitude and path of oil price changes are the same, RMB appreciation before depreciation still stimulates exports because of the time lag effect so that the regional output in A01 is slightly higher than that in A11.

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5.2. Impact on prices The domestic CPI fluctuates in the same direction as international oil prices and in the opposite direction of the RMB (see Tables B.1 and B.2). The impact of RMB exchange rate fluctuations on CPI is more pronounced. In 2023, oil prices in A01 are 124.67% higher than that in the baseline, which causes a small rise of 2%–4% in the CPI. In contrast, the opposite impact of the international oil price decrease on the domestic CPI is much more obvious. In scenario A02, oil prices decrease by 66.73% compared with the baseline in 2023, which is less than the degree of oil price increases in A01. This reduction still causes a 5%–8% fall in regional CPI. The differences between the impacts of rising and falling in oil prices on price indexes are so significant because firms use alternative inputs to replace oil when oil prices rise, which results in a reduction in the share of oil in input value and a further reduction in the impact of oil prices on output prices. However, when oil price fall, firms expand their oil inputs and reduce other inputs; therefore, falling oil prices increase the effect on production costs. In 2030, the regional CPI in A01 is 2%–4% higher than the baseline. At the same time, the regional differences in CPI are also growing. Among these regions, the northeast region is the most affected, and the central region is the least. 5.3. Impact on interregional trade Tables B.3 and B.4 in Appendix B show the changes of interregional import and export in 2023 compared with the baseline, respectively. When international oil prices rise (A01), the overall economy will slow down, and both interregional inputs and outputs will decline. The changes on interregional trade of oil are in opposite direction to that of other commodities. When international oil prices rise, buyers reduce their international purchase and increase domestic oil purchase. From the weighted average change of domestic trade, the promotional effect of the oil industry is less than the repressive effect of other industries, and most of the domestic trade in most regions is lower than that in the baseline.

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In scenario A03, RMB devaluation raises the international commodity prices in RMB until 2023; therefore, firms and residents are more inclined to buy domestic products. Additionally, regional imports and exports both increase. Specifically, in terms of interregional imports, the east has the most significant increase (2.01%), and the west has the smallest increase (1.66%). Interregional exports in the central region will increase significantly, and the increase in the northeast is slightly lower than that in the other regions. In the A11 composite scenario, the promotional effect of RMB depreciation on interregional imports and exports exceeds the repression effect of rising oil prices, thereby leading to increased domestic trade, and the northeast experiences the greatest increase in interregional import. In scenario A12, RMB devaluation and oil price decreases both contribute to the economic growth; consequently interregional imports and exports rise sharply.

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5.4. Other Results The results exhibit that the rise in real exchange rate is slightly higher than that in nominal exchange rate. In A01, high oil prices result in costdriven inflation in China, and consequently real exchange rate to rise slightly above the nominal exchange rate. When oil prices decrease, the real exchange rate changes in a direction opposite to that in A01 and shows a longer lag and greater change (see Fig.B.1). Not surprisingly, higher oil prices will lead oil sector to boom whereas all other sectors to decline. The electricity sector is most affected by oil price fluctuations because power output are strongly reliant on domestic power demand which is largely affected by oil price ups and downs. Regional electricity production requires little oil; thus, international oil price changes have a direct but small impact on power production costs. The impact is mainly driven by energy substitution and other industrial needs. The mechanisms are as follows: on the one hand, electricity and oil are partially substitutable energy sources; thus, rising oil prices will stimulate electricity demand; on the other hand, reductions in other industries caused by high oil prices will reduce the demand for power (see Tables B.5–B.8). We design two scenarios (S0 and S1) to capture the monetary authority’s response to oil price shocks: S0 represents the international oil price increase by 100% and no counterproductive monetary policy; S1 means international oil price increase by 100% and domestic interest rate rises by 10% (domestic interest rate rises by 10% of its base value). The results are presented in Tables A.5–A.8. Table A.5 represents a change in macro indicators, Table 20

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A.6–TableA.8 describe the changes of some regional variables. In each scenario, the results of this study are basically consistent with the findings by Liu et al. (2015): (1) For oil price hikes without any monetary policy, real GDP, employment, and real household consumption will decrease. This is because a 100% increase in oil price will raise production costs significantly, resulting in a 0.8830% increase in CPI and a 0.7150% increase in nominal GDP. Thus real GDP and real household consumption will decrease. When oil price rises, the nominal interest rate remains unchanged, thus the real interest rate decreases and the return of investment increases, stimulating the real investment and resulting in all regional investment increased by nearly 1% (Table A.8). (2) After introducing interest rate measures, inflation (measured by CPI) is depressed by 3.4547% and real GDP will decrease by 0.0017%. Real investment will also decrease further because the rise in the real interest rate is caused by the rise in nominal interest rate, resulting in higher investment costs and lower investment returns. Take the Northeast as an example, a 10% increase in interest rate will reduce investments by 10.13%. Simulation results for changes in real exchange rate, total industrial output and international trade are provided in Appendix B.

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6. Conclusions and discussion In this paper, a dynamic multi-sector multi-region dynamic CGE model is established to analyze the impact of the oil price fluctuations and exchange rate volatility on sectoral output, employment, prices, and regional economies in China. Allowing for certain substitutions among oil, coal, natural gas, electricity and other energies, a nine-layer nested production function is constructed. This paper adopts the latest available input-output table of China as the core basic data. Based on the original data, industries are categorized into eight sectors, including agriculture, construction, manufacturing, service, coal, oil, natural gas, and electricity, and provinces are merged into four regions: the northeast, east, west, and central regions. It is assumed that the RMB exchange rate would depreciate gradually to 25% by 2023 and then regress back to the 2016 level by 2030. The EIA’s projections on oil prices to formulate the lower and upper bounds of the price trend. In addition, two combinations on oil prices and RMB exchange rates change are formulated as two scenarios. The simulation results are as follows.

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(1) Both the RMB nominal exchange rate depreciation and international oil price increase will push up the domestic price level, of which in 2023 oil price increase causes 2–4% increases in regional CPI, whereas oil price decreases will help to curb domestic inflation by 5–8%. Therefore, the RMB real exchange rate reduction will be far less than the RMB nominal exchange rate reduction. Additionally, in high oil prices scenario, the real exchange rate will reach its highest level in 2025, representing a 2.59% increase above the baseline. The impact of decreasing oil prices on the real exchange rate will reach the summit in 2026 to 6.71% below the baseline. (2) By 2030, international oil price rises will decrease the GDP in the northeast by 0.31%, but almost have no impact on the GDP in the central region, widening the gap between the rich and the poor regions. These results are similar to Cross and Nguyen (2017) which considered that the impacts of the international oil price shocks on China’s output are small in nature. Furthermore, both falling oil prices and RMB devaluation have positive effects on regional GDPs, of which the GDP in the northeast increases most (1.43%). This is in contrast with Meng (2015) who finds that RMB appreciation contributes to China’s economic growth, more specifically, a 10% rise in RMB’s value will cause a 0.02% rise in GDP. The differences between our results and Meng (2015) can be explained by the unexpected increase in exports following an appreciation of RMB in Meng (2015), that is, by the strong import–export linkage in some sectors and the deflation in China caused by its currency appreciation. Additionally, results from the current model indicate that RMB devaluation will result in import to decline by 1.8–2.0% and export to expand by 1.0–1.5% in various regions, and the central region will be affected moderately. (3) By 2023, RMB devaluation will increase employment rates in all regions by almost 10%, while these values will decrease to below 1% in 2030. Similarly, oil price decreases will reduce the unemployment rates in the east, west and central regions, with the northeast is most affected by 2.75% in 2030. (4) By 2023, a large increase in international oil prices will cause a small rise of 2–4% in regional CPI. Du et al. (2010) concludes that a 100% oil price shock increases CPI by about 2.08%, which is consistent with our findings. Additionally, the reduction effects of oil price decreases will be 5–8% on CPI. The inflation effects in different regions arising from the RMB’s value changes will not be significant; in contrast, these effects of oil price fluctuations will be significant and vary across regions, in which the greatest impact will occur 22

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in the northeast region. (5) Oil price changes will have the greatest impact on the energy industries. When oil price increases, by 2030, oil production will increase by 4–9%, whereas the manufacture sector outputs of the central and northeast regions will decreases by 0.36% and 3.16%, respectively. Finally, by 2023, the positive impacts of RMB depreciation on agriculture, natural gas and coal will be 2%–4%, which is much more than that in the construction sector (about 0.5%). Overall, the economic consequence of an oil price drop is quantitatively greater than that of an oil price hike (in absolute terms) because in the former case, production and consumption will take full advantage of the lowered oil price, whereas in the latter case, imperfect energy substitution can only offset the oil price hike partially. The sensitivity analysis demonstrates that our simulating are robust against variations of the values of a few key variables (see Appendix D).

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Appendix A. Categorization of sectors and other basic data Table A.1: Categorization of sectors

Sectors in this paper

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Sectors in IO table

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Agriculture, Forestry, Animal Husbandry and Fishery Production and Supply of Electricity and heat Mining and Washing of Coal Manufacture of Non-metallic Mineral Products Processing of Petroleum, Coking and Processing of Nuclear Fuel Production and Supply of Gas and Water Smelting and Pressing of Ferrous Metals Mining and Processing of Non-metal Ores Chemical Industry Other Manufacture Mining and Processing of Metal Ores Manufacture of PaperPrinting and Articles for Culture, Education & Sport activities Manufacture of Textile Manufacture of Foods & Tobacco Processing of Timber & Manufacture of Furniture Manufacture of General and Special Purpose Machinery Manufacture and Processing of Metals and Metal Products Extraction of Petroleum and Natural Gas Extraction of Petroleum and Natural Gas Manufacture of Computers, Communication and Other Electronic Equipment Manufacture of Transport Equipment Instruments & Meters and culture & office machinery Manufacture of Textile, Wearing Apparel ,Footwear & Headwear Leather, and Feather Products Manufacture of Electrical Machinery and Apparatus Construction Leasing and Business Services Other Services Research & Development Hotels and Catering Services Transport and Storage Wholesale and Retail Trades

Agriculture Electricity Coal Manufacture Manufacture Manufacture Manufacture Manufacture Manufacture Manufacture Manufacture Manufacture

Manufacture Manufacture Manufacture Manufacture Manufacture Oil Gas Manufacture Manufacture Manufacture Manufacture Manufacture Construction Service Service Service Service Service Service

Table A.2: Main regional economic indicators in 2015 GDP

East Northeast Central West China

372983 57816 146950 145019 689052

PERGDP

IMGDP

EXGDP

NETEXGDP

INTDGDP

71019 52812 40274 39056 50127

24.71 8.14 3.9 4.44 15.83

32.26 7.12 7.3 8.58 21.43

7.55 -1.02 3.4 4.14 5.6

56.98 15.26 11.2 13.03 37.25

Source: National Bureau of Statistics of China. The meanings and scales of the indicators are as follows: GDP is in 108 RMB Yuan, PERGDP is per capita GDP (Yuan); imgdp is share of import in GDP(%); EXGDP is share of export in GDP(%); NETEXGDP is share of net export in GDP(%); INTDGDP is share of international trade in GDP(%).

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Export

Region

MAE

MAPE(%)

TIC

0.09 0.22 0.02 0.01

0.13 0.21 0.07 0.04

6.00 6.17 6.61 4.74

0.01 0.01 0.01 0.00

Northeast East West Central

0.01 0.21 0.01 0.01

0.04 0.19 0.04 0.04

5.39 5.94 5.56 5.57

0.01 0.01 0.01 0.01

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RMSE

Northeast East West Central

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Table A.3: Forecast error statistics

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Table A.4: Industrial structures in 2010 (%) Northeast

11.29 1.58 6.39 0.03 34.96 2.42 4.23 39.09 100.00

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Agriculture Coal Oil Gas Manufacture Electricity Construction Service Total

East

West

6.23 0.94 1.09 0.00 41.36 2.18 4.36 43.84 100.00

14.44 3.78 3.80 0.02 30.86 4.49 4.79 37.83 100.00

Central 13.17 3.57 0.20 0.00 38.43 3.20 4.98 36.46 100.00

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Table A.5: Changes in macro variables (%)

CPI Nominal GDP Real GDP

S0

S1

0.8830 0.7150 -0.1665

-2.5717 -2.7355 -0.1682

Table A.6: Changes in employment (%)

Northeast East West Central

S0

S1

-0.7478 -0.1646 -0.4259 0.0017

-0.7442 -0.1635 -0.4241 0.0018

Table A.7: Changes in household consumption (%)

Northeast East West Central

S0

S1

-0.3598 -0.0802 -0.2240 0.0009

-0.3580 -0.0796 -0.2230 0.0009

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S1

1.3391 0.9287 0.9776 0.8246

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Table A.8: Changes in total investment (%)

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Appendix B. Some simulating results

Table B.1: Changes in CPI from BAU in 2023 (%) A01

A02

A03

A11

A12

2.03 2.34 3.23 2.64

-5.05 -5.68 -7.69 -6.42

29.81 29.82 30.15 29.55

32.58 33.01 34.53 33.14

22.92 22.11 19.76 20.86

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Table B.2: Changes in CPI from BAU in 2030 (%)

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A01

A02

A03

A11

A12

2.03 2.35 3.28 2.69

-5.25 -6.08 -8.42 -6.86

-0.26 -0.32 -0.24 -0.20

1.79 2.05 3.06 2.51

-5.56 -6.45 -8.70 -7.11

Table B.3: Changes in inter-regional imports from BAU in 2023 (%)

Central East Northeast West

A01

A02

A03

A11

A12

-1.08 -1.42 0.31 -0.09

3.14 4.22 -0.65 0.42

1.96 2.01 1.77 1.66

0.82 0.49 2.07 1.55

5.26 6.49 1.14 2.13

Table B.4: Changes in inter-regional exports from BAU in 2023 (%)

Central East Northeast West

A01

A02

A03

A11

A12

-0.45 -0.35 -2.23 -1.58

1.3 1.05 7.24 4.73

2.19 1.77 1.75 1.85

1.68 1.38 -0.59 0.17

3.61 2.89 9.35 6.83

Appendix B.1. Impact on real exchange rate Normally, changes in the nominal exchange rate are not in line with that of the real exchange rate (Asteriou et al., 2016). The real exchange rate is 26

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adjusted according to the price level of two countries (see Eqs.(20)–(21)). Fig.B.1 reveals that the volatility of the RMB real exchange rate (in indirect quotation) is much smaller than that of the nominal rate. In scenario A03, the RMB nominal exchange rate in 2023 is 25%, which is lower than that in the baseline. Because RMB depreciation will lead to a rise in domestic prices, after price adjustment, the real exchange rate reduces by only 3.09%. In the scenario where oil prices decrease (A02), the real exchange rate changes in a direction opposite to that in A01 and shows a longer lag and greater change. The impact of decreasing oil prices on the real exchange rate reaches summit in 2026 at 6.71% below the baseline. However, the effect of decreasing oil prices is much larger than that of rising oil prices (2.59%). In scenario A11, the decreasing effect of RMB nominal exchange rate depreciation on the real exchange rate is slightly larger than the pulling effect of rising oil prices in 2023 and before. The real exchange rate is approximately 0.5% lower than the baseline. After 2024, the proportion of oil prices tends to stabilize in A11 and BAU, and the impact of oil prices on the real exchange rate is very small. A01

2 0

A02

A03

A11

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A12

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-4 -6 -8

-10

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Fig. B.1: Change of real exchange rate from BAU (%)

Appendix B.2. Impact on total industrial output Tables B.5 and B.6 present the industrial output effects arising from international oil price shocks. The differences among industries show that oil prices directly benefit the oil industry. Oil production increases by 4–9% compared with BAU in 2030. The two reasons are: first, import oil prices rise, and then residents and downstream firms purchase foreign oil instead of domestic oil; and second, export price decreases enhance export competitive advantage and increase international demand for China’s oil. 27

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Regarding sectors other than oil, most of the outputs are lower than they are in the baseline. When domestic product prices rise because of higher production costs, the demand of firms and consumers for other international goods excluding oil increases, whereas the demand for domestic goods decreases. In the manufacturing sector, for example, 9.59% of the manufacturing output in the northeast is from exports, and 6.75% is from imports. The proportions of manufacturing exports and imports in the central region are 5.39% and 5.35%, respectively, which are much smaller than those in the northeast. Consequently, the impact of international oil prices on manufacturing output is much more serious in the northeast than that in the central region. The electricity sector is most affected by international oil price fluctuations, and electricity prices in each region are distinctive in the degree and direction of change. Because power transport requires a grid, and the import and export of the power industry account for a low share of power output, changes in output are strongly reliant on changes in domestic demand. Regional electricity production requires little oil; thus, international oil price changes have a direct but small impact on power production costs. In addition to international trade and production costs, the impact channels of international oil prices on power industry also include energy substitution and other industrial needs. In scenario A02, the decline in global oil prices is not conducive to the output of the domestic oil industry but is beneficial to most of the other industries. The direction of the change of each region’s output is opposite to that in A01, and the amplitude is obviously larger. The reason is analogous to why the effect of oil price decreases is significantly greater than that of oil price rise mentioned above and is similar to the conclusions of Cross and Nguyen (2017). Table B.5: Change in industrial output in A01 from BAU in 2030 (%)

Agriculture Construction Coal Electricity Gas Manufacture Oil Service

Central

East

Northeast

West

-0.22 -0.16 -2.55 0.44 -1.1 -0.36 8.55 -0.28

-0.84 -0.32 -4.05 0.35 -3.66 -1.29 7.63 -0.72

-2.76 -0.6 -4.26 -0.05 -4.82 -3.16 4.46 -1.41

-1.25 -0.52 -3.53 -0.07 -4.22 -2.33 6.31 -1.06

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0.55 0.41 6.5 -0.85 2.62 0.88 -22.24 0.77

East 2.21 0.84 11.64 -0.71 9.94 3.42 -20.26 2.03

Northeast 7.87 1.61 12.36 0.55 13.98 8.92 -13.11 4.25

West

3.48 1.38 10.09 0.33 11.77 6.35 -17.57 3.1

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Central Agriculture Construction Coal Electricity Gas Manufacture Oil Service

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Table B.6: Change in industrial output in A02 from BAU in 2030 (%)

AC CE

PT

ED

MA

NU

Tables B.7 and B.8 present the industrial output resulting from the RMB exchange rate in 2023 and 2030, respectively. Until 2023, more industrial output is affected by exchange rate devaluation than in the baseline. However, the increasing range in different industries is not the same, and the differences across one industry in different regions are also great. The main factors that affect regional output are the following: (1) the proportion of exports in outputwhen the proportion is greater, the pulling effect of total output is more obvious; (2) the share of import goods in local goods, with higher shares corresponding to more domestic products bought by firms and greater outputs; (3) the substitution elasticity between domestic sales and export and domestic purchase and import, with larger substitution elasticity corresponding to greater manufacturer flexibility to adjust the purchase structure and sales structure and greater industrial output; and (4) industrial production structure. Because RMB devaluation is equivalent to an increase in import prices, a higher share of import inputs in production costs results in a greater difficulty in firm expansion. Table B.7: Change in industrial output in A03 from BAU in 2023 (%)

Agriculture Construction Coal Electricity Gas Manufacture Oil Service

Central

East

Northeast

West

3.64 0.69 3.33 2.03 2.87 2.10 1.57 1.94

4.07 0.56 2.42 2.33 2.71 1.83 1.48 1.78

2.77 0.41 3.02 1.91 2.61 1.56 1.50 1.57

3.85 0.40 1.83 1.31 2.15 1.51 1.29 2.15

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Northeast

0.16 0.07 1.42 1.94 1.72 0.27 1.55 0.29

0.24 0.05 1.56 1.68 1.62 0.27 1.38 0.26

0.11 0.05 1.63 1.87 1.44 0.26 1.13 0.16

West

0.00 0.00 1.15 1.26 1.03 0.14 0.87 0.12

NU

SC

Central Agriculture Construction Coal Electricity Gas Manufacture Oil Service

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Table B.8: Change in industrial output in A03 from BAU in 2030 (%)

AC CE

PT

ED

MA

Appendix B.3. Impact on international trade Since the domestic oil production sector concentrate in the northeast and the west, the international oil price shocks will affect their regional economy more seriously, especially the northeast (see Table B.9—TableB.12). In the high oil price scenario (A01), rising oil prices lead to an overall macroeconomic downturn, in which both imports and exports decrease. Because of high production costs, the decline in exports is generally greater than that in imports. Compared with global goods with constant prices, the production costs of other commodities fall with the decline in oil prices, resulting in dramatically increasing competitiveness in these commodities, which will increase exports and reduce imports. In the scenario A03, RMB devaluation will make the region’s exports increase by 1.0–1.5% over the period 2017–2023 and imports decrease by 1.8–2.0%. In the A11 and A12 composite scenarios, the change in imports and exports in each region is related to the difference in oil endowments. In the northeast and the west, the simulation results in the composite scenario are similar to those in the oil price increase scenario (A01) and oil price decrease scenario (A02), respectively. In the central and eastern regions, the results are comparable to those in the scenario with RMB fluctuation (A03). Table B.9: Changes in imports from BAU in 2023 (%)

Central East Northeast West

A01

A02

A03

A11

A12

0.73 -0.40 -6.29 -4.58

-2.19 0.70 18.93 12.89

-1.93 -1.81 -1.86 -1.75

-1.22 -2.21 -8.28 -6.46

-4.08 -1.12 17.55 11.52

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A01

A02

A03

A11

0.70 -0.43 -6.27 -4.55

-1.99 0.83 18.17 12.47

-0.20 -0.25 -0.24 -0.22

0.47 -0.70 -6.60 -4.85

A12

-2.11 0.66 18.24 12.53

SC

Central East Northeast West

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Table B.10: Changes in imports from BAU in 2030 (%)

A01

A02

A03

A11

A12

A01 -1.05 -1.28 -1.86 -1.47

A02 2.83 3.56 5.39 4.24

A03 1.24 1.10 1.11 1.15

A11 0.12 -0.27 -0.85 -0.41

A12 4.24 4.86 6.78 5.63

MA

Central East Northeast West

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Table B.11: Changes in emports from BAU in 2023 (%)

ED

Table B.12: Changes in exports from BAU in 2030 (%) A02

A03

A11

A12

-1.03 -1.25 -1.83 -1.46

2.77 3.36 5.22 4.16

0.12 0.13 0.11 0.08

-0.93 -1.14 -1.73 -1.38

2.92 3.53 5.38 4.27

PT

Central East Northeast West

A01

AC CE

Appendix C. Yearly simulation results Table C.1: Changes in GDP by region in A01 from BAU (%) Years

Central

East

Northeast

West

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

-0.01 -0.03 -0.05 -0.08 -0.09 -0.10 -0.10 -0.10 -0.10 -0.09 -0.09 -0.09 -0.09 -0.09

-0.04 -0.08 -0.11 -0.14 -0.15 -0.16 -0.17 -0.17 -0.17 -0.16 -0.16 -0.16 -0.16 -0.16

-0.12 -0.21 -0.27 -0.31 -0.32 -0.33 -0.35 -0.31 -0.30 -0.28 -0.28 -0.29 -0.30 -0.31

-0.08 -0.15 -0.20 -0.24 -0.25 -0.26 -0.28 -0.25 -0.24 -0.23 -0.23 -0.24 -0.24 -0.25

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East

Northeast

West

0.00 -0.01 -0.01 0.00 0.03 0.07 0.12 0.15 0.18 0.19 0.19 0.18 0.17 0.15

-0.06 -0.12 -0.15 -0.17 -0.17 -0.16 -0.15 -0.11 -0.09 -0.08 -0.09 -0.10 -0.11 -0.12

-0.28 -0.51 -0.69 -0.82 -0.90 -0.97 -1.03 -0.95 -0.91 -0.89 -0.89 -0.92 -0.94 -0.96

-0.15 -0.29 -0.39 -0.46 -0.49 -0.51 -0.53 -0.47 -0.44 -0.43 -0.44 -0.45 -0.48 -0.50

SC

Central

NU

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

MA

Years

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Table C.2: Changes in employment by region in A01 from BAU (%)

Table C.3: Changes in GDP by region in A02 from BAU (%) Years

Central

East

Northeast

West

0.01 0.05 0.10 0.16 0.22 0.27 0.31 0.35 0.36 0.34 0.31 0.29 0.28 0.28

0.06 0.13 0.21 0.30 0.39 0.48 0.58 0.59 0.59 0.57 0.55 0.55 0.55 0.55

0.17 0.35 0.55 0.76 0.95 1.17 1.43 1.36 1.27 1.17 1.12 1.1 1.11 1.13

0.12 0.25 0.39 0.56 0.71 0.88 1.08 1.07 1.04 0.98 0.94 0.92 0.92 0.93

AC CE

PT

ED

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Table C.4: Changes in employment by region in A02 from BAU (%) Years 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Central

East

Northeast

West

0.00 0.00 0.01 0.00 -0.04 -0.11 -0.19 -0.25 -0.33 -0.42 -0.48 -0.50 -0.49 -0.45

0.09 0.19 0.28 0.37 0.43 0.50 0.57 0.48 0.40 0.32 0.29 0.28 0.30 0.33

0.41 0.85 1.30 1.80 2.28 2.82 3.43 3.22 3.02 2.84 2.73 2.70 2.72 2.75

0.23 0.47 0.72 0.99 1.24 1.51 1.80 1.68 1.55 1.43 1.36 1.34 1.36 1.41

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East

Northeast

West

0.34 0.63 0.89 1.12 1.39 1.66 1.91 1.71 1.49 1.29 1.05 0.78 0.49 0.17

0.31 0.58 0.81 1.01 1.25 1.48 1.69 1.49 1.29 1.09 0.88 0.63 0.37 0.09

0.27 0.51 0.73 0.91 1.14 1.35 1.56 1.40 1.23 1.08 0.89 0.67 0.44 0.19

0.33 0.61 0.85 1.06 1.31 1.55 1.77 1.57 1.35 1.16 0.94 0.69 0.43 0.15

SC

Central

NU

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

MA

Years

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Table C.5: Changes in GDP by region in A03 from BAU (%)

Table C.6: Changes in employment by region in A03 from BAU (%) Years

Central

East

Northeast

West

1.36 2.72 4.09 5.49 6.94 8.41 9.90 8.59 7.26 5.91 4.54 3.13 1.71 0.29

1.32 2.64 3.97 5.31 6.70 8.11 9.53 8.23 6.92 5.61 4.28 2.93 1.57 0.2

1.25 2.51 3.78 5.08 6.42 7.78 9.16 7.94 6.70 5.44 4.16 2.86 1.55 0.23

1.33 2.65 3.99 5.34 6.73 8.14 9.56 8.23 6.90 5.57 4.22 2.87 1.51 0.15

AC CE

PT

ED

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Table C.7: Changes in GDP by region in A11 from BAU (%) Years 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Central

East

Northeast

West

0.33 0.60 0.83 1.03 1.28 1.53 1.77 1.58 1.37 1.18 0.95 0.68 0.39 0.08

0.27 0.50 0.69 0.86 1.08 1.29 1.49 1.30 1.10 0.92 0.71 0.48 0.22 -0.05

0.16 0.30 0.45 0.59 0.79 0.99 1.18 1.07 0.93 0.79 0.61 0.40 0.17 -0.08

0.25 0.46 0.64 0.81 1.04 1.26 1.46 1.29 1.09 0.92 0.71 0.47 0.21 -0.07

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East

Northeast

West

1.35 2.71 4.08 5.49 6.97 8.48 10.02 8.74 7.44 6.11 4.73 3.32 1.88 0.44

1.25 2.52 3.80 5.12 6.51 7.92 9.35 8.09 6.81 5.51 4.18 2.82 1.45 0.09

0.97 1.98 3.05 4.19 5.43 6.70 7.99 6.88 5.70 4.49 3.21 1.91 0.59 -0.72

1.17 2.35 3.58 4.84 6.18 7.55 8.94 7.69 6.41 5.10 3.76 2.40 1.03 -0.33

SC

Central

NU

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

MA

Years

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Table C.8: Changes in employment by region in A11 from BAU (%)

Table C.9: Changes in GDP by region in A12 from BAU (%) Years

Central

East

Northeast

West

0.35 0.68 1.00 1.30 1.64 1.97 2.29 2.13 1.92 1.68 1.40 1.10 0.77 0.44

0.37 0.71 1.03 1.33 1.66 2.00 2.34 2.13 1.91 1.68 1.43 1.16 0.88 0.58

0.45 0.87 1.28 1.69 2.11 2.56 3.05 2.79 2.51 2.22 1.95 1.69 1.43 1.16

0.44 0.86 1.26 1.64 2.05 2.48 2.92 2.68 2.42 2.14 1.86 1.57 1.28 0.98

AC CE

PT

ED

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Table C.10: Changes in employment by region in A12 from BAU (%)

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Central

East

Northeast

West

1.36 2.72 4.10 5.48 6.89 8.30 9.71 8.33 6.92 5.49 4.05 2.62 1.21 -0.18

1.42 2.84 4.27 5.71 7.18 8.68 10.20 8.78 7.37 5.97 4.58 3.22 1.86 0.51

1.67 3.39 5.17 7.02 8.92 10.93 13.05 11.50 9.97 8.47 7.03 5.64 4.29 2.95

1.56 3.14 4.76 6.41 8.09 9.82 11.61 10.09 8.58 7.09 5.63 4.22 2.86 1.52

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136

SC

126 BAU A01 A02 116

NU

A03 A11

MA

106

96

ED

Total GDP (trillion yuan)

A12

AC CE

PT

86

76

66

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

Fig. C.1: Total outputs in all scenarios from 2017 to 2030

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SC

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35

BAU 30

A01

NU

A02

A11

MA

A12 25

ED

Total imports (trillion yuan

A03

AC CE

PT

20

15

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Fig. C.2: Total imports in all scenarios from 2017 to 2030

36

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A03

A11

A12

NU

A02

MA

A01

ED

42.0

BAU

SC

52.0

22.0

AC CE

PT

32.0

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Fig. C.3: Total exports in all scenarios from 2017 to 2030

Appendix D. Sensitivity analysis To demonstrate the robustness of our model, a sensitivity analysis is performed. Some important parameters are altered, specifically the substitution elasticity between oil and gas in production, the substitution elasticity between energy and capital in production, the substitution elasticity between domestic and imported oil. After altering parameters, the scenarios A01 and A03 are recalculated. The detailed assignment and simulation results for sensitivity analysis are exhibited in Table D.1 and Table D.2 respectively. 37

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As to the substitution elasticity between oil and gas (cases SA1 and SA2), no variable deviates form case SA0 more than 0.001 percentage points in both scenario A01 and A03. Take the elaborative production structure into consideration, the substitution elasticity between oil and gas is only one of so many elasticities, that its variation has negligible impacts on the simulation results. With regard to the substitution elasticity between domestic and imported oil, all the variables in scenario A03 change no more than 0.03 percentage points and could be ignored. Real exchange rate of RMB and nominal GDP diverge from case SA0 about one percentage point, while real GDP diverges about 0.1 percentage point and other variables diverges 0.1–1 percentage point. The larger this parameter is, the greater the impacts are, especially for price index, such as CPI. However, after deducting inflation effect, the real GDP is quite stable and almost immune from this parameter alteration. Regarding to the substitution elasticity between energy and capital, the simulation results are also stable as no variable change from the case SA0 more than half a percentage point. To sum up, these important variables are weakly impacted by the changes in parameters analyzed; as a result the simulation results are robust.

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Table D.1: Assignment of sensitivity analysis Case

Percentage change from SA0

Normal assignment in the model

SA0 SA1 SA2 SA3 SA4 SA5 SA6

Normal assignment in the model 50% -50% 50% -50% 50% -50%

AC CE

Altered parameter

Substitution elasticity between oil and gas Substitution elasticity between domestic and imported oil Substitution elasticity between energy and capital

Table D.2: Simulation results of sensitivity analysis

Scenario

Variable

SA0

SA1

SA2

SA3

SA4

SA5

SA6

A01

Real GDP Nominal GDP CPI Real Exchange Rate Import Export

-0.1776 2.3080 2.4343 2.4900 -0.8663 -1.2698

-0.1775 2.3072 2.4336 2.4891 -0.8659 -1.2691

-0.1776 2.3089 2.4351 2.4909 -0.8668 -1.2704

-0.0729 3.3584 3.2218 3.4338 -1.4580 -1.6282

-0.2893 1.1431 1.5730 1.4365 -0.1987 -0.8833

-0.2263 2.4491 2.6089 2.6815 -0.8190 -1.3370

-0.0730 1.9156 1.9998 1.9901 -0.9066 -1.0453

A03

Real GDP Nominal GDP CPI Real Exchange Rate Import Export

0.1282 -0.1643 -0.2771 -0.2921 -0.2439 0.1254

0.1279 -0.1640 -0.2766 -0.2915 -0.2432 0.1252

0.1283 -0.1644 -0.2773 -0.2924 -0.2443 0.1255

0.1283 -0.1424 -0.2589 -0.2703 -0.2489 0.1171

0.1272 -0.1817 -0.2913 -0.3085 -0.2389 0.1312

0.3209 -0.3580 -0.5810 -0.6767 -0.5473 0.2989

0.0079 -0.0184 -0.0339 -0.0264 -0.0303 0.0090

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