key: cord-0972765-yxnp0kc3
authors: Wang, Qiang; Zhang, Fuyu
title: What does the China's economic recovery after COVID-19 pandemic mean for the economic growth and energy consumption of other countries?
date: 2021-02-10
journal: J Clean Prod
DOI: 10.1016/j.jclepro.2021.126265
sha: 598350154c96e30ae7815a89c5dbffc90c9f6e45
doc_id: 972765
cord_uid: yxnp0kc3

China is the first major economy to show a recovery after a slowdown induced by the COVID-19 pandemic. This work aims to explore what the China’s economic recovery after the COVID-19 pandemic means for the economic growth and energy consumption of the other countries using the global VAR quarterly data. In the long term, spillover effects of China's economic growth have the most obvious impact on upper-middle-income countries’ economic growth (0.17%), followed by the economic growth of lower-middle-income countries (0.16%) and high-income countries (0.15%). However, the spillover effect of China’s economic growth has the most significant impact on energy consumption in high-income countries (0.11%∼0.45%), followed by energy consumption in upper-middle-income countries (0.08%∼0.33%) and in lower-middle-income countries (-0.02%∼0.05%). Our results indicate upper-middle-income countries will benefit the most from China’s economic recovery post-COVID-19, followed by lower-middle-income countries and high-income countries. The spillover effect of China’s economic recovery post-COVID-19 brings the most obvious impact on the increase in energy consumption in high-income countries, followed by middle-income countries. It also should be noted that the spillover effect of China's economic growth does not necessarily lead to an increase in energy consumption lower-middle-income countries. Generally, the spillover effect of China’s economic recovery on other countries’ economic growth is much more than other countries’ energy consumption.

between countries cannot be ignored in the relationship between economy and energy consumption, especially for major economies like China.

In this context, this work is dedicated to exploring the impact of China's economic recovery after COVID-19 pandemic on economic growth and energy consumption in other countries through spillover effects. In this regard, we use a large number of empirical results in 24 countries and the European Union area (including 8 countries) from 1991 to 2014 to speculate on the impact of China's economic recovery after the COVID-19 pandemic on the economic growth and energy consumption of other countries. This is of great significance to the global economic and enenrgy recovery after the COVID-19. In order to deeply analyze the spillover effects of China, selected countries are divided into three income-level groups and particularly classifying as major trading partners and non-major trading partners to China within each income-level group.

The remainder of this paper is arranged as follow. In section 2, we summarize the relevant literature. In section 3, we describe method and present data. In section 4, we conduct empirical results specifically. In section 5, heterogeneity in spillover effects of economic growth in China is discussed. Finally, in section 6, we come to conclusions.

With the global economic downturn, scholars switched their focus to explore economic changes brought by the pandemic. Ibn-Mohammed et al made an J o u r n a l P r e -p r o o f overreview of positive and negative impact of COVID-19 from various perspective, and specificly discussed the barries and opportunities for circular economy (Ibn-Mohammed et al., 2020) . Kabir et al found that economic dip caused by the COVID-19 was closely related with child mortality since major donor countries for humanitarian programs were badly hit by the pandemic (Kabir et al., 2020) . From the financial perspective, Sharif et al studied relationship between the COVID-19 outbreak, oil price, stock market, economic uncertainty, and geopolitical risk of the US economy accorfing to the wavelet-based approach. The results turned out that the US policymaker shall pay much more attention on economic uncertainty as well as geopolitical risk (Sharif et al., 2020) . Norouzi et al analyzed the impact of pandemic on China's economy with discussing petroleum and electricity demand. They discovered that the COVID-19 damaged China's economy both in a short and long term. Fortunately, it is likely to increase the ration of renewable energy in energy consumption due to energy security (Norouzi et al., 2020) . Mamun and Ullah picked a novel perspective to consider the impact of COVID-19 in Pakistan. They found lockdown-related economic recession is highly related with most of suicide by researching the connection between suicide and the COVID-19 (Mamun and Ullah, 2020) . Khurshid and Khan investiated how the COVID-19 shock Pakistan's economic development and energy consumtion. Based on that, they also applied the system dynamic modeling approach to forecast what will Pakistan's economic development and energy consumtion be like untill 2032 (Khurshid and Khan) .

Regarding the impact of COVID-19 on the energy, many scholars have studied J o u r n a l P r e -p r o o f the changes in the energy field after the emergence of COVID-19 (Abu-Rayash and Dincer, 2020) . In a study on the energy consumption required for personal protective equipment and test kits, it was concluded that the total energy demand dropped sharply during the outbreak of the epidemic due to reduced commercial and industrial activities. In addition, the increase in energy demand of many households has exacerbated the initial energy poverty (Klemeš et al., 2020) . In a study on Europea, Bahmanyar et al. compared the impact of different containment measures taken against the COVID-19 epidemic on its power consumption, and the results supported the work in countries with strict restrictions, such as Spain, Italy, Belgium and the United Kingdom. Daily energy consumption has been greatly reduced (Bahmanyar et al., 2020) . In addition, the research results of Sovacool et al. showed that mandatory lockdown or quarantine has significantly linked the COVID-19 pandemic to energy demand and greenhouse gas emissions (Sovacool et al., 2020) . From an environmental point of view, the reduction of economic activities and traffic restrictions have directly led to changes in China's energy consumption, thereby preventing environmental pollution. COVID-19 has improved China's air quality in a short period of time (Wang and Su, 2020) .

Though a number of studies have focused on the global economic and energy changes brought about by COVID-19, most studies have focused on the interior of a single country or collective but have ignored the interconnection between countries.

In fact, in the context of economic globalization, the country is no longer an island (Chica-Olmo et al., 2020) . In previous studies in other fields, the global vector J o u r n a l P r e -p r o o f autoregressive (GVAR) method was used to assess the relationship between economies (Chudik and Fratzscher, 2010) . Feldkircher and Korhonen considered that how shock from the world economy influence China's economy and how shock from China influence the world economy on the basis of GVAR model. They found China's economic growth benefited its trading partners (Feldkircher and Korhonen, 2012) . Gurara and Ncube unveiled that global growth spillover on Africa mostly came from Eurozone and BRICS economies (Gurara and Ncube, 2013) . Kempa and Khan aimed to explore two-way spillover effects of public debt and growth in eurozone by GVAR model on the basis of quarter data from 1991Q2 to 2014Q4 (Kempa and Khan, 2017) . Timo Bettendorf developed a GVAR model to identify spillover effects of credit default risk in nine European Union members, Japan, the United States, and the United Kingdom. The results indicated the spillovers presented stronger within European Union members (Timo Bettendorf, 2019).

Thus, the global VAR method has been widely used in the international spread of shocks. GVAR model is available to identify interdependencies between economies which shaped through internal global trade (Samargandi et al., 2020) . Moreover, it enables researchers to focus on international transmission of shocks (Timo Bettendorf, 2019). Our purpose is to conduct an international research on the spillover effects of the Chinese economy on other countries' economic growth and energy consumption after the COVID-19 epidemic, and the GVAR method is fully applicable to our research. Hence, in order to specifically investigate spillover effect of China on other economies when shocked by the COVID-19, the GVAR model is applied.

In conclusion, this work has contributed to the existing literature in the following two aspects. First, the application of the GVAR method accurately captures the spillover effects of China's economic recovery on other countries. Different from previous studies, multilateral nature of international interlinkages is considered.

Therefore, this study provides new insights into the relationship between economic growth and energy consumption at an international level. Second, this work uses data from 33 countries covering more than 90% of global GDP, and provides country-by-country analysis of 40 quarters of economic growth and energy consumption response curves. The results help to identify the specific impact of China's economic recovery after the COVID-19 epidemic on the economic growth and energy consumption of other countries in the long term.

The GVAR framework has been widely recognized in the analysis of interdependence in a multinational environment (Pesaran et al., 2001) . The model is constructed using VECM in a single country or region. First, each country or region establishes its own domestic macroeconomic factors. Second, establish links between economies by including simultaneously interrelated variables. To capture the most dynamic in the world economy, the country-specific weight vector autoregression models interlinked through trade rights. Therefore, the implementation of the GVAR framework involves two main steps. The first step is to predict individual J o u r n a l P r e -p r o o f country-specific VECM, which includes foreign factors. The second step is to use trade weights to superimpose each country model into the GVAR model.

The GVAR model assumes that there are N+1 countries, represented by n=0,1,2, 3, ..., N. The 0th country is the reference country. 

Where , represents the coefficient related to specific foreign variables, which is a × matrix. , represents the impact of a specific country, it is a vector. 

This equation can be transformed into:

Where , -= ∑ , where is a k × 1 vector included in the non-dominant variable model (k = ∑ " ). 9 : is a < = × k weight matrix, < = is the global cross-sectional average value:

Assuming that there is no cointegration relationship between the common vector and the section average > & , Eq. (4) can be written as:

Where ? & is estimated by the least square method. It is worth noting that ∆ > does not include the value of the same period in Eq. (5).

J o u r n a l P r e -p r o o f

The GVAR model is proposed in (Dées et al., 2007; Dées et al., 2006; Pesaran et al., 2004) . Feldkircher applied this method to the spillover effects of monetary policy (Benecká et al., 2020) and rate of return in the euro area.

These evaluations of the predictive performance of the GVAR model are carried out on a variable-by-variable basis, that is, from a univariate perspective. In addition,

Feldkircher also proposes a series of variables (for example, the GDP growth of all countries) or examines the forecast of the joint forecast density of the entire system (Dovern et al., 2016) . In contrast, the method provided in this work is univariate prediction of GVAR. Different from Feldkircher's model, we include carbon emissions from the World Bank and energy consumption from BP into the model, thus expanding the GVARTOOLBOX database.

The GVAR model is composed of the VAR model of a single country. Trade, finance or distance can all be used as the construction of the connection matrix between domestic and foreign variables. Trade-weighted foreign variables were used in this study. Firstly, we define the / + * 0 × 1vector @ , = / , , , * 0 " and assume that = , Eq. (5) can be written as: Secondly, the individual country variables are aggregated into global variables.

The global variable is a vector of k×1. I = GI , -, I , -, … , I ", -H -, where k = ∑ " is the total number of endogenous variables in the global model. Next, a single country variable can be written as: @ , = 9 I , K = 0,1,2, … , M

Where 9 is a × / + * 0 fixed constant matrix defined by the weight of a specific country. 9 plays a role in linking a single country variable with a foreign variable.

Third, substitute Eq. (7) into equation Eq. (6):

A , 9 I = , + , + A , 9 @ , + B , , + ,

Where A , 9 and A , 9 are × k matrices. Adding these equations together can get a "global" solution:

The conditional single country model Eq.

(1) and the marginal model Eq. (5) can be combined as a complete GVAR model.

The current value and lag value at this time both appear on the right side of equation (8), and i = max / 0 , s = max / 0 , A = R A 9 A 9 … A " 9 " S ,

S . = / -, I -0′ and is / + k0 × 1

Vector. Eq. (4) and Eq. (8) can be written as:

J o u r n a l P r e -p r o o f

Finally, since T is a full−rank k × k dimensional matrix dimensional matrix, it is a non-singular matrix. Therefore, all variables in the GVAR model can be written as:

] is a square lower triangular matrix, which shows the causal nature of the dominant variable . Solving Eq. (10) forward recursively, the future value of can be obtained.

The GVAR model of this study used data in quarterly frequency for 24 countries and the EU area (including 8 countries) covering Europe, Asia, Latin America, Africa and Oceania. One of the benefits of the GVAR model is that it enables homogeneous economies into a bloc that can be treated as a single region. The Euro area (Austria, Belgium, Finland, France, Germany, Italy, Netherlands, and Spain) is pooled into one regional in the model. The sample of countries covers the period between 1991Q2 and 2014Q4. The World Bank divides these countries into three income levels, as shown in Table 1 .

Taking into account the sample gaps in different levels of countries, we processed the data logarithmically to reduce data fluctuations and eliminate heteroscedasticity. In addition, the first-order difference processing improves the stability of the data, thus contributing to more accurate calculations (Li et al., 2019) .

The annual data is converted into quarterly data through EViews10 to ensure the Global variables. All domestic variables will be affected by global variables. This study uses oil price as a proxy for global factor. The impact of oil price fluctuations will have a non-negligible impact on any country, which are also called exogenous shock variables. 

This section introduces the dynamic analysis of the spillover effects of economic growth in China. We select GDP and energy consumption (EC) as economic and energy indicators respectively. The GVAR model is used to simulate the shock on selected variables and generate responses to GDP and energy consumption in the model. Specifically, exchange rate (EP), energy consumption structure (ENS), and carbon emissions (CE) are control variables for the potential transmission mechanism between China's GDP and the GDP and energy consumption of other countries.

According to the World Bank classification criteria, this study divides countries into three categories: high-income countries (HI), upper-middle-income countries (UMI), and low-middle-income countries (LMI). Therefore, this section is divided into three subsections. In addition, according to the trade weight matrix obtained from the In each subsection, we analyze the responses of major trading partners and non-major trading partners respectively.

The current study used the Generalized Impulse Response Function (GIRFs)

proposed by Koop, Pesaran, and Potter (Koop et al., 1996) , and is suitable for the VAR models introduced by Pesaran and Shin (Pesaran and Shin, 1998 These results are shown in Table A .1-A.10 in Appendix A.

The 14 economies in this study are classified as HI countries. There are seven major trading partners in HI countries: USA, the European Union, Japan, Korea, the United Kingdom, Australia, and Canada. Fig. 1 and Fig. 2 display the economic and energy GIRFs related to a positive shock to economic variable in China and its spillover effects on the major trading partners in HI countries, respectively. Generally speaking, the spillover effects is positive for major trading partners in the HI countries.

The GIRFs curve presents a positive dynamic response, and the response is not J o u r n a l P r e -p r o o f constant. The following is a detailed description.

USA, the European Union, and Japan, as China's top three trading partners. First, with respect to immediate GDP response to the spillover effects of economic growth in China, USA, the European Union, and Japan responded to the positive shock of the GDP in China by 0.32%, 0.53%, and 0.66% in quarter 0, respectively. In addition, the energy consumption responded to 0.07%, 0.15%, and 0.28%, respectively. Among them, Japan's immediate response was the highest. Subsequently, we found that the response value of GDP is decreasing but the response value of energy consumption is increasing after quarter 0. Second, for the long-term response to the spillover effects of economic growth in China. Specific to the GDP response of each country, the response value of USA (0.12%) is higher than European Union (0.02%) after the quarter 4. Subsequently, the positive response of GDP in Japan increased to 0.27% compared to almost no fluctuation in USA and European Union in the quarter 8.

Furthermore, specific to the energy response of each country, the response values of various countries increased first and then decreased, and stabilized after quarter 8. 

In current study, non-major trading partners in HI countries include Chile, New

Zealand, Norway, Saudi Arabia, Singapore, Sweden and Switzerland. Fig. 3 On the whole, their GIRFs curves show a transition from volatility to stability, which indicates that the spillover effect of China economic growth is a long-term process.

In this task, we first investigate the immediate response to the spillover effects of 

Two countries in LMI countries are selected as samples for this study: India and the Philippines. India is major trading partners of China in LMI countries. Fig. 9 and 

In terms of the Philippines, it is non-major trading partners in LMI countries.

Overall, Philippines presents significant and statistical positive responses to the shock of economic growth in China. In detail, the immediate GDP and energy response to the spillover effects of economic growth in China are 0.25% and 0% respectively in quarter 0. Hereafter, the GDP and energy response value of the Philippines increased to 0.53% and 0.29% respectively in quarter 1. Subsequently, the long-term GDP and energy response value decreases and finally stabilizes at 0.31% and 0.23%, respectively. Finally, this response persisted for 40 quarters.

In the previous section, we observed that the economic and energy dynamic 

This subsection summarizes the dynamic responses and provides a comprehensive overview of the spillover effects of China's economic growth. The purpose is to find out the similarities of spillover effects of economic growth in China to different countries. obvious that curves can be divided into two sections according to the response change trend. Quarter 6 is the turning point, and the two curves around quarter 6 present completely different information. More specifically, before quarter 6, more than 80% of countries had the largest response value in quarter 0. This suggests that when

China's economy fluctuates, other countries will be subject to greater immediate impact. Subsequently, as the quarter increases, this spillover effect begins to decrease and is accompanied by small fluctuations. In other words, the positive spillover effect on China's economic growth only plays a significant role in a limited period. After quarter 6, the long-term response of more than 85% of the countries is far lower than their immediate response. Then the long-term response remains stable at the lowest point. This implies that the spillover effect of China's economic growth in the long-term is relatively weak. Regarding economic spillover effects, the RMB spillover effects between participating countries of the "The Belt and Road" initiative have been identified (Wei et al., 2020) .

In short, the spillover effect of China's economic growth on other countries is a long-term process, and it is positive for more than 90% of countries. The spillover effects of economic growth in China can be divided into two stages: the immediate response stage and the long-term response stage. The immediate response is the most significant. After quarter 6, the immediate response is transformed into a long-term response. The long-term response is much lower than the immediate response, which J o u r n a l P r e -p r o o f exists in 34 quarters. On the whole, the curve shows an overall trend of rise → decrease → stable. This is similar to the U.S. EPU impact on the output of other economies that increased for the first time in a few months, but declined after about two or three months (Trung, 2019) .

To be precise, the energy response curves of more than 90% of countries showed an upward trend in the quarter 0. Subsequently, the maximum response values of the entire process are reached approximately in quarter 2. Next, almost all countries' response values fell in quarter 2 -6. In the end, the response curve remained stable and J o u r n a l P r e -p r o o f stabilized at the relatively low position in quarter 6-40. This implies that China's GDP growth has a positive spillover effect on energy consumption in most countries.

Besides, this positive effect will reach the maximum after 2 quarters. But in the long run, China's GDP growth will maintain a slight positive spillover to energy consumption in most countries. In previous studies, there is a spatial correlation between GDP and renewable energy consumption, but this correlation has only been confirmed in 26 EU countries. Regarding the mechanism of spillover effects, the "contagion" between neighboring countries leads to spatial dependence as the key In conclusion, the spillover effect of China's economic growth on energy consumption is positive for more than 80% of countries and the response curve presents an inverted U shape. The immediate and long-term response of China's economic spillover effects to energy consumption are relatively small throughout the process. At the junction (approximately quarter 2) of the immediate and the long-term effect, the positive response value of the spillover effect is the largest.

Sample countries were divided into 3 groups according to income level to perform GIRFs analysis in Section 3. We judge the spillover effects of economic growth in China based on the response value. According to the results, if the response J o u r n a l P r e -p r o o f valuec 0, the spillover effect is positive, otherwise it is negative. The results prove that spillover effects of economic growth in China are generally positive and long-term processes. However, does this spillover effect consistent across countries?

To answer this question, we discuss the heterogeneity in spillover effects of economic growth in China across income levels in this subsection. We find out the heterogeneity of spillover effects by comparing the GIRFs curve between different income levels. Fig.14 and Fig.15 show the economic and energy GIRFs curves for the three income levels. The response value of each income level is the mean value of the response value of the corresponding countries. After about a year, evidence shows that the long-term response of the spillover effects on LMI countries gradually increases while on HI and UMI countries decrease. In quarter 30, the average response values of LMI and UMI countries were 0.16% and 0.17%, respectively, and the HI countries had the smallest response value (0.15%).

These results indicate that the long-term response of spillover effects of economic growth in China to LMI and UMI countries is higher than that of HI countries.

In summary, the study found that the spillover effects of economic growth in China are heterogeneous among countries with different income levels. From the perspective of immediate response, the spillover effect is more obvious in HI and UMI countries than in LMI countries. In other words, LMI countries are less sensitive to positive shocks of economic growth in China. But from the perspective of long-term response, the spillover effects are more obvious to LMI countries than in HI

and UMI countries. This shows that HI and UMI countries have passed some positive shocks to LMI countries after a period of time. This finding is not surprising.

Similarly, the spillover effects of the U.S. EPU vary from country to country.

Developing and emerging economies are more vulnerable than advanced economies.

Nguyen Ba Trung pointed out that emerging Asian economies are more susceptible to US EPU shocks because the region has a higher share of trade with the US economy (Trung, 2019) . 

The prevention and control policies to fight COVID-19 has triggered the global economic recession and energy challenges in 2020. Thanks to the early effective control, China has become the first major economy to recover from COVID-19. This study sought to investigates the impact of GDP growth in China on economic and energy of other countries, in order to test the spillover effects of China's economic recovery among various countries. The empirical strategy adopted in this study was an application of a GVAR model, which can examine the international spillover effects of shocks on the basis of the interdependence between economies. Notably, 24 countries and the EU area (including eight countries) are selected and divided into three income levels to obtain a heterogeneous assessment of the spillover effects of China's economic recovery.

First, an dynamic analysis of spillover effects overview of economic growth in Our conclusions support that China will play an important role in the global economic recovery after COVID-19 epidemic. To get rid of the economic and energy crisis caused by COVID-19 epidemic, international exchanges and cooperation are essential. In particular, China's right to speak in the field of international economy and energy utilization should be enhanced. Furthermore, decision makers at the global and national levels need to jointly respond to and manage global energy.

The GVAR model can predict economic changes in a range of countries.

However, due to the availability of data, this work can only simulate empirical results from 1991-2014 data to speculate on China's impact on economic growth and energy consumption in other countries after the COVID-19 epidemic. Taking into account the repetitive nature of the COVID-19 epidemic, the control measures that some governments have to continue to take after the peak period have had an impact on local economic development and energy consumption, which cannot be simulated by our model. In the future, we can consider using updated data during the COVID-19 epidemic to solve this problem. J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f 

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The authors would like to thank the editor and these anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the manuscript. This work is supported by National Natural Science