The macro-economic and CO₂ emissions impacts of COVID-19 and recovery policies in China

Abstract

The outbreak and ongoing evolution of the COVID-19 pandemic have dramatically impacted economic development and CO₂ emissions. China is under simultaneous pressure to recover from the outbreak and meet its carbon reduction targets, and the government is endeavouring to stimulate economic recovery through fiscal and monetary policies. This paper uses a computable general equilibrium model to measure the impact on China’s economic recovery and carbon emissions by incorporating the pandemic shock and related economic recovery policies of loan prime rate (LPR) and value-added tax (VAT) reduction. The study found that COVID-19 led to a simultaneous shock on China’s supply and demand sides in which GDP dropped by 2.62% and carbon emissions fell by 2.53%, compared to the period prior to COVID-19. Although the LPR and VAT reduction effectively mitigated economic loss, the combined LPR and VAT reduction had a more substantial effect on boosting GDP than the single policies. The VAT cut expands production and was used to overcome supply-side shocks, while lowering LPR mitigates the damage of demand-side shocks. Compared to the VAT reduction policy, reduced LPR has smaller carbon emissions per unit of GDP output. Consequently, we recommend that the government concentrate on a combination of policies to navigate pandemic shocks, as the two economic stimulus policies are confirmed to complement one another in terms of strengths and shortcomings.

1. Introduction

The international COVID-19 outbreak has not only threatened human lives but has also affected the socio-economy and environment on which humanity depends (Klemeš et al., 2020, Le Quéré et al., 2020). At the beginning of the COVID-19 pandemic, to control the spread of the virus, many countries adopted mandatory restrictions, such as social distancing, closures, quarantines and restrictions on production activities, which severely damaged global socio-economic operations (Dong et al., 2022, Nicola et al., 2020, Xu and Wei, 2021). These restrictions led to significant changes in energy consumption and CO₂ emissions (Shan et al., 2021, Zhang et al., 2022a). During COVID-19 restrictions, people spent more time working from home and engaging in online activities more frequently, which reduced the CO₂ generated by social mobility, such as commuting or travelling outside of the home (Zhou et al., 2020). In China, energy-related carbon emissions were abated by 18.7% (182 Mt CO₂) in the first quarter of 2020; however, economic policy stimuli may boost CO₂ emissions in the coming years (Wang et al., 2020). In short, the COVID-19 pandemic could result in slight mitigation of global greenhouse gas emissions in the short term (Zhang et al., 2021).

The evolution and development of the virus continues. It is undeniable that the heterogeneous negative supply shocks triggered by public health-related prevention and control activities severely impacted aggregate output, and the global economy could face prolonged recession (Baqaee and Farhi, 2020, Guan et al., 2020, Mandel and Veetil, 2020). It is important to note that fossil fuels contribute to global warming, and the energy structure in China is dominated by coal, the leading cause of greenhouse gas emissions (Dietzenbacher et al., 2020). The green targets (dual control of total energy consumption and intensity) of the 14th Five-Year Plan, representing a crucial step towards achieving carbon peak and neutrality, may be weakened to achieve economic recovery after the COVID-19 crisis (Gosens and Jotzo, 2020).

The Chinese government faces simultaneous pressures to recover from the pandemic and meet carbon reduction targets. To achieve carbon peak and neutrality, many provinces in China have begun to adopt policies restricting electricity production. The Chinese government is also applying fiscal and monetary policies, such as tax cuts for enterprises and lower interest rates on loans, to revive the economy from COVID-19, resulting in a post-COVID reproduction, consumption and CO₂ emissions increase, which is compelling a slower pace of economic recovery for emissions reduction (Liu et al., 2021b). In the context of COVID-19, the recovery of the Chinese economy will require a highly effective policy or combination of policies to effectively navigate the above problems. The government must develop policies that favour a low-carbon path, promote sustainable development and avoid a carbon emission rebound effect.

To date, the COVID-19 crisis remains highly uncertain, and there is potential for several waves of future pandemics due to ongoing mutations. The global economic outlook depends on whether the pandemic continues or countries can contain COVID-19 and mitigate its impact on the global economy through selected policies. Notably, some countries have begun to loosen pandemic prevention and control in favour of economic recovery and development, even directly reverting to non-COVID-19 living conditions. China continues to adhere to strict prevention and control policies, such as the closure of Shanghai, temporary suspensions of public transport and in-home isolation of residents to combat the virus. These measures result in tremendous economic sacrifice. To prevent economic depression, China is currently undertaking a recovery plan to better shape its economic future. For example, in terms of monetary policy, on 20 February 2020, the People’s Bank of China (PBOC) cut the one-year loan prime rate (LPR) by 10 basis points to 4.05% and the five-year or longer LPR by five basis points to 4.75%. These prudent monetary policies will release liquidity and lower financing costs in the capital market, which is expected to ease funding gaps for enterprises and stimulate investor confidence (Liu et al., 2021b). In terms of fiscal policy implementation, the large-scale value-added tax (VAT) rebate policy from 1 April 2022 is equivalent to an additional cash flow of about RMB 1.5 trillion for market participants, which will powerfully help enterprises to lighten production and operational burdens (GOV, 2022a). While responding to the COVID-19 crisis, it is also essential to consider the impact of recovery policy implementation on China’s carbon reduction targets. A green strategy for economic recovery is essential to fully leverage this opportunity for better recovery and building a sustainable economic society.

There remains limited quantitative research on the COVID-19 pandemic, especially on developing policies to both mitigate greenhouse gas emissions and aid economic recovery (Lahcen et al., 2020). The macro-economic computable general equilibrium (CGE) model is valuable for studying this issue, as the model can assess the impact of policy choices in response to the pandemic by identifying the main economic channels for analysing potential impacts on economic activity and carbon emissions (Hartono et al., 2021). Therefore, this paper established a dynamic CGE model for China based on 2018 input–output tables and referred to the method used in Pan (2016), Zhang et al. (2022b) and Liang et al. (2022), to simulate and analyse the effects of COVID-19 and economic recovery initiatives (VAT tax cuts and LPR cuts) on the Chinese economy and carbon emissions. Unlike previous studies of the impact of disasters on the economy and society, our model captures the overall macro-economic impact and environmental mitigation effects of the two recovery policies and policy combination.

The remainder of this paper is organised as follows. Section 2 provides a review of the existing literature and presents the innovations of this study. The model and data sources are described in Section 3. Section 4 presents the results analyses and a comparison of the seven scenarios constructed for the study. Finally, Section 5 details our conclusions and policy recommendations.

2. Literature review

As COVID-19 mutates and spreads worldwide, with outbreaks occurring one after another, an increasing number of scholars have been examining the relationship between resulting economic stagnation and CO₂ emissions reduction (Martins et al., 2020, Tello-Leal and Macías-Hernández, 2021). Previous studies have demonstrated a positive linear relationship between CO₂ emissions and GDP (Wang et al., 2019, Wen et al., 2021). Most of these studies were conducted using micro-economic or econometric methods. Smith et al. (2021) used a global vector autoregressive (GVAR) model to assess the impact of world fossil fuel consumption on national climate change targets, revealing that COVID-19 led to a decrease in energy consumption in the world’s major carbon-emitting countries. Han et al. (2021) used national and provincial GDP data and Chinese emissions accounts and datasets to estimate emissions reductions in the first quarter (Q1) of 2020, finding a reduction in CO₂ emissions of 257.7 Mt (11.0%) compared to Q1, 2019. While these measures capture the transient and dramatic nature of COVID-19, they have the disadvantage of ignoring the interactions between relevant sectors within the socio-economic system.

Suvarna et al. (2022) used a linear input–output model to estimate the daily supply of coal-fired thermal power plants in India due to the COVID-19, demonstrating that nearly 26% electricity reduction could lead to a loss of 10%–31% in GDP and a reduction in CO₂ emissions of about 15%–65%. Shan et al. (2021) adopted a global input–output model to study the impact of the COVID-19 pandemic and fiscal measures on the global economy and CO₂ emissions, determining that CO₂ emissions would be reduced by 3.9%–5.6% over five years (2020–2024) compared to the baseline scenario excluding COVID-19. The authors also found that existing fiscal policies increased global emissions from −6.6 to 23.2 Gt (−4.7% to 16.4%) over five years, depending on the strength and structure of incentives. The input–output model can capture both direct economic losses from COVID-19 and the indirect impact of the pandemic on the industry. The CGE model based on general equilibrium theory considers the economic behaviour of individual agents, adjusting market prices in the model to achieve a new market general equilibrium, which is more suitable for analysing fiscal and monetary policies (Guo and Shi, 2021, Wei et al., 2019).

Scholars are increasingly applying CGE models to study COVID-19 and related recovery policies (Hartono et al., 2021, Malliet et al., 2020, Porsse et al., 2020). Liu et al. (2021a) used a global CGE model to explore the impact of individual fiscal policies and policy combinations with carbon taxes on the economic environment, finding that COVID-19 shocks decreased global economic development in 2020 and this state continued in 2021, further impeding economic activity. Among government recovery policies, the authors determine that indirect tax cuts provide the best positive economic stimulus but are detrimental to low-carbon energy development and raise CO₂ emissions. They recommended that a post-pandemic green recovery plan could prioritise the replacement of indirect production taxes with a tax on greenhouse gas emissions. Notably, in this model, the advantages and disadvantages of countries’ different taxes were not investigated. At the country level, Pradhan and Ghosh (2021) adopted a recursive dynamic CGE model to analyse the impact of COVID-19 and the previously existing tax on coal production (GST offset tax) and high taxes on petroleum products in India. Porsse et al. (2020) used CGE modelling to predict the impact of the COVID-19 pandemic and fiscal responses on the Brazilian economy. The study demonstrated that government fiscal stimulus measures partially mitigated GDP losses under different COVID-19 severity levels. Lahcen et al. (2020) applied a CGE model to analyse the energy efficiency impact of public subsidies on the building sector, demonstrating a positive economic impact of green recovery in Belgium.

In China, Xu and Wei (2021) constructed a multi-regional CGE model to assess the short- and medium-term impacts of COVID-19 on the macro-economy, energy and environment. The results indicated that (1) in the absence of tax incentives, GDP, consumption, exports and production output experienced weak growth in 2021, and imports and consumer price index significantly declined in 2022. (2) If tax cuts were implemented, GDP would grow by 2.83% and 7.4% in 2020 and 2021, respectively, and imports, exports and output would increase significantly, raising fossil fuel consumption and carbon emissions. Using the VAT rate reduction in China in 2018–2019 as an example, Guo and Shi (2021) analysed the driving effect of VAT cuts on economic development and local fiscal pressure, constructing a CGE model to examine COVID-19. The results indicate that local fiscal pressure increases 27.08%, from 0.342 to 0.435; however, the CGE model does not introduce COVID-19 pandemic shocks. Liu et al. (2021b) used the CGE model to quantify COVID-19 pandemic shocks and policy responses, emphasising the trade-off between the impact of monetary and fiscal policies. The study demonstrated that both monetary and fiscal policy responses are effective in mitigating economic damage to GDP and employment; however, both policies have adverse side effects, such as increasing consumer prices by 1.05% and 0.57%, respectively and declines in exports of 2.61% and 1.05%, respectively. Monetary policies can exacerbate the damage to external demand from pandemic supply-side shocks but are better suited to mitigating demand-side shocks. Whereas fiscal policy would benefit nearly all productive sectors, monetary policy would negatively impact the export-oriented manufacturing sector. Nevertheless, the study does not consider energy use and climate change.

Previous research on COVID-19, economic recovery policies and carbon emissions have several shortcomings. Although scholars in various countries have studied COVID-19 shocks and government countermeasures on the economy and carbon emissions, few have comparatively analysed the advantages and disadvantages of fiscal and monetary policies and the trade-offs between the two on changes in economic variables and carbon emissions. Unlike previous research focusing on China, our innovations and contributions are threefold. First, based on COVID-19 shocks, we concentrate on the impact of individual fiscal and monetary policies and policy combinations on China’s economic recovery and carbon emissions. We pioneer a unique quantitative analysis of the mechanisms by which fiscal and monetary policies (VAT and LPR cuts) affect carbon emissions in the industry, thus exploring the impact of policies on the efficiency of emissions reduction. Second, we apply a dynamic CGE model to assess the synergistic effects of these monetary and fiscal responses by combining them with the supply- and demand-side shocks of the pandemic, to provide more appropriate and feasible policy recommendations for future economic development and the achievement of China’s dual carbon targets. Third, as the future evolution of COVID-19 remains highly uncertain, based on the 2018 input–output table, the dynamic CGE model simulates and analyses the impacts of COVID-19 on economic development, energy consumption and CO₂ emissions in 2020. The results will be valuable for nations worldwide to effectively adopt targeted responses to public health emergencies with fewer negative externalities.

3. Methodology and data sources

3.1. CGE model

The CGE model is a panoramic quantitative simulation approach based on general equilibrium theory (Walras, 2014), structural macro-economic relationships and national accounts to describe the functioning of an economic system. The model can be used to study the long-term, deterministic behaviour of an overall economy and its changing response to external shocks. As a policy-analysis tool, the CGE model comprehensively examines the impact of shocks on an overall economic system, where changes in any part of the system affect the system overall, leading to changes in commodity and factor prices and quantities, as well as the transitions from one equilibrium state to another (Wing, 2011).

To simulate the impact of COVID-19 and its policy responses in China, we construct a dynamic CGE model, including production, income, expenditure, trade and closure modules. All equations and endogenous and exogenous variables of the model are presented in Appendix B, and we detail a brief description of the model structure below.

The production module describes the relationship between factor inputs and production outputs in China’s production sectors. The model assumes a perfectly competitive market in which market equilibrium conditions determine sectoral output and production decisions according to the principle of cost minimisation. The production module applies a multi-layer nested design to reflect and manage the more complex substitution relationships between multiple inputs. The first nesting level consists of intermediate inputs and composite factors solved through the constant elasticity of substitution (CES) function. The second layer consists of two components, (a) the intermediate input compounded by the less than function and (b) the capital–labour bundle compounded by the CES function.

The income and expenditure module consist of two main entities of residents and the government. Residents and government maximise utility through the Cobb–Douglas utility function subject to an income function constraint. Residents’ income, which is used for consumption or savings after paying income taxes, is derived from labour income, capital remuneration and government transfers. Government revenue comes from taxes on production, consumption, VAT, income taxes and import tariffs and its expenditures include the purchase of goods, transfers and government savings.

The domestic supply of goods comes from domestic production and imports for final demand and intermediate consumption. To achieve the lowest consumption cost, when purchasing goods, a rational consumer will optimise the mix of domestic and imported goods. The Armington condition is satisfied between the two, as there is imperfect substitution between imports and domestic products. Gross domestic output is sold domestically and abroad under the profit maximisation principle, and producers choose between the domestic market and exporting, using a fixed constant elasticity of transformation (CET) function for the combination (Zhang et al., 2022a).

In addition, the CGE model requires a micro-closure and a macro-closure to maintain a unique solution to the model, ensuring that the constraints are consistent with the number of endogenous variables. The model uses neoclassical closures to establish closures in commodity markets, factor markets, institutional income and expenditure, foreign markets and investment savings, including supply and demand equilibrium in the commodity and factor markets, residents’ total consumption calculated as disposable income minus savings, total government savings equalling government revenue minus consumption and transfers to residents, total investment equalling total savings and the difference between exports and imports for foreign investment.

3.2. Data sources

This paper constructs a 2018 China Social Accounting Matrix table according to our research needs, including industrial sectors, commodities, factors (labour, capital), institutions (residents, government), the rest of the world and investment savings. The data are obtained from the National Input–Output Tables for 153 sectors in 2018, the China Finance Yearbook 2018 and the China Taxation Yearbook 2018.

We calculate most of the exogenous parameters in the CGE model using a calibration method based on input–output table data and relevant formulae. The remaining parameters are exogenously assigned, including the elasticity of substitution of the production function, the Armington function and the CET function, the sectoral capital depreciation rate, population growth rate, capital growth rate and technological progress. The values are primarily referred to in other CGE studies, such as Dai et al. (2018), Zhang et al. (2018) and Jia et al. (2021).

4. Scenario setting for COVID-19 and countermeasures

4.1. The supply- and demand-side shocks of the COVID-19 pandemic

COVID-19 exerted severe impacts on both supply and demand sides (see Fig. 1). On the supply side, the model simulates COVID-19 labour supply and sectoral total factor productivity (TFP) shocks. (1) Regarding labour supply shocks, it is calculated by multiplying the number of infected people derived from the model by the number of days lost without work, and the number of deaths due to illness is deducted from the labour growth as mortality. The number of infected workers is separated from the overall infected population using the proportion of working-age workers and the labour force participation rate. According to data from the National Bureau of Statistics as of 31 December 2020, there are 90,597 cumulative recoveries and 4789 cumulative deaths. China’s total population at the end of 2020 was 1.44 billion, and the labour force aged 15 to 64 was 940 million. Based on this, it can be calculated that the proportion of the labour force in China in 2020 will be roughly 65% (9.4/14.4). The labour force participation rate was 79%, and the percentage of infected workers was 52.1% (65% *79%). We assume that the infected labour force will be absent from work for 20 days, including five days of sickness, eight days of hospitalisation and an additional week of absence prior to returning to work; thus, the labour shock in 2020 is 2.26%. (2) Total factor productivity: sectoral TFP shocks are based on delayed return to work from the COVID-19 pandemic which can be obtained using the number of delayed days of returning to work compared with the number of days worked in a quarter. As the timing of resumption of work and production varies across provinces, the national average workday loss is approximately 6.9 days, calculated by multiplying the delayed return to workdays by each province’s share of national GDP, accounting for 2.74% of 2020 workdays. We also assume that industries of national importance, such as agriculture, forestry and fishing, food and tobacco, medical products, production and supply of electricity and heat, production and supply of gas, production and supply of water and the health sector, will not be affected.

Fig. 1.

COVID-19 shock in the CGE model.

On the demand side, we simulate changes in consumption propensity, risk premiums and international trade shocks. (1) Changes in consumption propensity are represented by quarterly changes in sectoral consumption. We refer to the consumption measurement method of Duan et al. (2021) for each sector and select the rate of change in consumption propensity. Then, based on the consumption panic index under COVID-19, adjust the amount of consumption propensity changes. Specifically, consumption for textiles, clothes, shoes and leather (−11.9%), transportation, warehouse and post (−7%), hotels and dining (−26.2%), health care (−5.8%) and cultural, sporting and recreational activity and service (−26.2%) were all reduced, while FOD (4.9%) increased. (2) Risk premium. We reference McKibbin and Fernando (2021) and Liu et al. (2021a), reducing the risk premium of investment by 1.97%. (3) Regarding international trade shocks, referencing Duan et al. (2021), we assume that the world market price of exports expressed in foreign currency increases by 0.5% during the COVID-19 pandemic.

4.2. Monetary and fiscal policies for the COVID-19 pandemic

The outbreak will bring multiple severe economic challenges, including slower economic growth, unemployment and poverty, business bankruptcies and reduced government revenue. Hence, the government must expediently manage the resulting health and economic crises. Monetary easing and active fiscal policies are necessary to mitigate the economic damage caused by COVID-19. Monetary easing policy will release liquidity, reduce financing costs to ease corporate funding shortages and boost investor confidence in the market, whereas active fiscal policy will finance the prevention and control of the pandemic, reduce the tax burden on businesses and expand government spending to stabilise employment and economic growth. Considering China’s real economy, we select two specific fiscal and monetary policy tools for analysis, including VAT reduction, LPR reduction and a combination of the two.

VAT reduction refers to the government’s reducing enterprises’ VAT rate to expand adequate supply and promote economic growth. VAT reduction will reduce the tax burden borne by enterprises for production, increasing enterprises’ liquidity and profit level; however, the government collects less VAT revenue, which poses a more serious challenge to the treasury. The effect of the VAT reduction on enterprises is often challenging to measure, as there are homogeneous differences in tax burden pass-through to different enterprises. According to tax attribution theory, in a perfectly competitive market, the magnitude of a VAT reduction is the same as the reduction in commodity prices if demand is perfectly elastic and supply is perfectly inelastic. The policy dividend of such tax reductions is often not available to intermediate players with weaker bargaining power because the tax reduction reduces the price of the goods they sell, but not necessarily the tax-inclusive price of intermediate inputs purchased from upstream firms.

In comparison, LPR is the interest rate that commercial banks charge the most creditworthy corporate customers, or those with the highest credit ratings, based on the open market operating rates and representative banks’ loan rates. The National Interbank Offered Rate Centre, authorised by the PBOC, publishes this data to guide all financial institutions. The reduction in LPR is one of the critical tools of monetary policy, indicating that enterprises can borrow from banks at a lower price to reduce financing costs. LPR reduction has a significant role in stabilising expectations and boosting market participants’ confidence.

As Table 1 shows, the study integrates the characteristics of COVID-19, establishing a non-pandemic scenario, COVID-19 shock and seven policy response scenarios to simulate the impact of the COVID-19 on China’s macro-economy and carbon emissions and the recovery effect of countermeasures compared to the non-COVID-19 shock. We set the policy scenarios of model based on representative response recovery policies implemented by the Chinese government against the pandemic. In China, the government implements a series of macro response measures to boost the economy. According to China’s State Taxation Administration (STA, 2020), from 1 March 2020 to 31 December 2021, the VAT rate for small-scale taxpayers is reduced from 3% to 1% nationally, except that Hubei province which has been hit hardest by the early COVID-19 outbreak exempt this tax. Therefore, we set a general scenario in which the VAT for all production sectors are reduced from the original 3% to 1% (i.e., a VAT rate reduction of 60%, as in scenario VAT-ALL). To examine the economic effect of the fiscal policy on different type of sub-sectors, we also set a VAT-I scenario in which the VAT reduction policy is only applied to the industrial sector and a VAT-S scenario in which the VAT reduction policy is only applied to the service sector. In terms of monetary policy, according to the People’s Bank of China, on 20 February 2020, the one-year LPR is decreased from 4.15% to 4.05%. On 20 April 2020, the LPR lowered further to 3.85%, dropping again to 3.8% on 20 December 2021. Based on the above policy, we set a 10-basis point reduction (0.1%) in LPR (a 2.4% reduction in financing costs) as scenario LPR-10bp. To examine the sensitivity of monetary policy, we also set the LPR-20bp scenario with a 20-basis point (0.2%) reduction in LPR and the LPR-30bp scenario with a 30-basis point (0.3%) reduction in LPR.

Table 1.

Scenario setting.

Name	Code	Description
Business as usual (BAU)	No COVID-19	Business develops as usual without COVID-19
Pandemic shock	COVID-19	COVID-19 occurs and the government does not establish any economic recovery policies
VAT cut	VAT-ALL	VAT for all sectors reduced from the original 3% to 1%
	VAT-I	VAT in the industrial sector reduced from the original 3% to 1%
	VAT-S	VAT in the services sector reduced from the original 3% to 1%
LPR reduction	LPR-10bp	Assume a 10-basis point reduction in Loan Prime Rate
	LPR-20bp	Assume a 20-basis point reduction in Loan Prime Rate
	LPR-30bp	Assume a 30-basis point reduction in Loan Prime Rate
Combined policy	VAT-LPR	Combined VAT-ALL and LPR-10bp

5. Results and discussions

5.1. Impact of COVID-19 on China’s macro-economy and CO₂ emissions

COVID-19 caused a loss of labour supply and stagnation of factor flows on the supply side, while also depressing consumption capacity, investment intentions and international trade on the demand side. This dual supply and demand shock amplified the negative impact of the pandemic on the economy. Consistent with expectations, the COVID-19 pandemic has clearly impacted the Chinese economy (see Fig. 2), causing a 2.62% decline in GDP compared to the baseline scenario without the pandemic. According to the forecast of the International Monetary Fund Report World Economic Outlook published in the end of 2019, China’s GDP growth rate will be 5.8% in 2020 in the absence of the pandemic (IMF, 2019). The official release GDP growth rate of 2.3% in 2020 imply a 3.3% decline rate base on IMF’s projection, which is similar to our simulation result. Of the total decline of GDP, −1.69% is caused by the supply side and −0.94% by the demand side. The GDP loss from the pandemic caused by supply-side shocks is more pronounced, as the decline in consumption is significantly larger than that of investment and exports. Residential consumption decreased by 5.56% and government consumption decreased by 1.71%, primarily due to direct decreases in consumption caused by the pandemic and prevention-control policies and indirect decreases in consumption caused by reductions in production and supply. In addition, enterprises were affected by reductions in operating income due to increased risk premiums and investment decreased by 1.97%. Exports and imports decreased by 2.99% and 6.66%, respectively, due to reduced domestic production, higher product prices and depressed foreign markets.

Fig. 2.

The changes in the macro-economy from COVID-19 in China (%).

The output of various industries presents different degrees of decline under the COVID-19 shock. Fig. 3 presents the movement of each industry’s output in China relative to the BAU scenario. The hotels and dining and transportation, warehouse and post sectors are the most severely damaged by the negative impact of pandemic prevention and control measures, sluggish domestic demand and foreign travel bans, with sectoral output declining by 11.47% and 4.02%, respectively, compared to the base scenario. The decrease in output was primarily due to a lack of demand. Similarly, the construction sector is directly and negatively affected by social distancing constraints and indirectly affected by the supply chain to the real estate sector, with which it is highly associated. Due to the increased demand for medical supplies and necessities during the pandemic, the output of the food and tobacco, pharmaceutical products and health industries increases by 1.38%, 3.79% and 2.48%, respectively.

Fig. 3.

Sectoral output changes from COVID-19 shocks (%).

Due to overall contractions in production and consumption, China’s carbon emissions would be reduced by 2.53% without response measures. Among these effects, supply-side shocks lead to a 67% carbon reduction, and demand-side shocks lead to a 34% carbon reduction. In terms of sectoral carbon emissions, the COVID-19 shock reduces sectoral output, resulting in decreased carbon emissions. The top 10 sectoral emission reductions simulated by the dynamic CGE model are displayed in Table 2. In the carbon module, in Eq. (B.20), $QXE{N}_a$ represents the fossil energy input of each sector, while $em{f}_q$ represents the carbon emission coefficient of various fossil energy sources. It allows us to calculate the carbon emissions of each industry under the BAU and shock policy scenarios. The carbon emission reduction for each sector is the difference between the BAU and the policy scenario for each sector’s carbon emissions. Specifically, the electricity production and supply sector contribute the most to reducing emissions, with 129.84 Mt, accounting for 55.86%, followed by the upstream construction industry and the metal manufacturing industry, which produces basic materials, with a 36.13 Mt (15.54%) emissions reduction. Among the services sector, transportation, warehouse and post had the most considerable emissions reduction, with 23.76 Mt (5.6%). Two factors caused the emissions decline in transportation. First, international and domestic public transportation, such as air, rail and bus passenger flow, was restricted. Second, freight transport was also significantly reduced due to reductions in factory production and contractions in global trade. The transportation sector is closely followed by wholesale and retail trade, which reduced emissions by 2.52 Mt (1.08%) and hotel and dining, which reduced emissions by 2.24 Mt (0.96%). Carbon emissions from the energy production and supply sector, which is the basis of both production and living, decreased by 3.40%. High international coal prices generated a tight supply of coal and electricity and reduced energy consumption due to the shutdown of enterprises and commercial activities. Notably, COVID-19 will not reduce the residential sector’s energy demand.

Table 2.

Top 10 sectors with CO₂ emissions reduced.

Rank	Sector	CO2 emission reduction (million tonnes, Mt)	The proportion of total CO2 emission reduction
1	Electricity production and supply	129.84	55.86
2	Metal smelting and product manufacturing	36.13	15.54
3	Transportation, warehouse and post	23.76	10.22
4	Non-metal product manufacturing	23.19	9.98
5	Chemicals manufacturing	3.53	1.52
6	Coal, petroleum and gas products	3.40	1.46
7	Wholesale and retail trade	2.52	1.08
8	Hotels and dining	2.24	0.96
9	Agriculture	1.20	0.52
10	Textiles, clothes, shoes and leather	1.00	0.43

5.2. Impact of policy stimuli on China’s macro-economy and emissions

The economic downturn caused by COVID-19 is mitigated by implementing policy response stimuli. As shown in Table 3, scenarios VAT-All, VAT-I and VAT-S increase GDP by 0.27%, 0.25% and 0.52%, respectively, relative to the effects of no policy. Among the fiscal policies, the comprehensive tax cute of the VAT-All scenario have the largest effect in boosting GDP, followed by VAT-I. In contrast, the VAT cuts on services alone of the VAT-S scenario do not effectively mitigate the supply-side shock caused by the pandemic. The GDP boosting effect of monetary policy is positively correlated with its intensity of implementation. Scenarios LRP-10bp, LRP-20bp and LRP-30bp increase GDP by 0.25%, 0.28% and 0.30%, respectively, relative to the absence of policy. As demonstrated in Table 3, scenarios VAT-ALL, LRP-10bp and VAT-LPR (the combined policy of VAT-All and LRP-10bp) increase residential consumption by 1.14%, 0.25% and 1.39%, respectively, and government consumption by −11.40%, 0.18% and −11.25%, respectively. The reduction in LPR has a superior effect on residential consumption demand than the VAT cut because it generates intertemporal household substitution effects on consumption and does not squeeze out private consumption. The tax cut is equivalent to ‘sacrificing’ part of the government’s revenue for enterprise production, which leads to a decrease in government consumption, while lowering the LPR has a positive effect on government consumption. Table 3 shows that investment grows 3.65% under VAT-ALL compared to the COVID-19 shock scenario. In comparison, investment grows 0.27% under the LPR-10bp scenario than in the COVID-19 scenario, indicating that investment per unit of GDP grows faster under the VAT cut than under the LPR reduction.

Table 3.

Macro-economic variables of recovery policies compared to COVID-19 shock (%).

	VAT-ALL	VAT-I	VAT-S	LPR-10bp	LPR-20bp	LPR-30bp	VAT-LPR
GDP	0.27	0.21	0.06	0.25	0.28	0.30	0.52
Final consumption	−2.73	−2.12	−0.56	0.23	0.26	0.27	−2.51
Household	1.14	0.66	0.38	0.25	0.28	0.30	1.39
Government	−1.40	−8.33	−2.66	0.18	0.20	0.21	−11.25
Investment	3.65	2.87	0.71	0.27	0.30	0.32	3.93
Export	3.35	2.71	0.58	0.28	0.31	0.33	3.64
Import	3.16	2.65	0.44	0.27	0.31	0.33	3.44
CPI	0.54	0.76	−0.22	0.03	0.03	0.03	0.57
CO2 emissions	4.22	3.40	0.74	0.26	0.29	0.32	4.49

VAT and LPR cuts can contribute to higher aggregate output; however, the VAT reduction policy does not positively affect production in all industries. In Fig. 4, for the scenarios VAT-All, VAT-I and VAT-S, pharmaceuticals manufacturing (8), medical equipment manufacturing (12), water and environment infrastructure service (26), education (28), health care (29), social work (30), cultural, sporting and recreational activity and service (31) and public administration, social security and organisation (Sec 32) all have reduced output. Lowering LPR has a positive effect on production in the agriculture, industry and service sectors, but the effect on output is not significant. This suggests that the government should develop economic recovery policies that consider industries’ added value, positions in the industrial chain and the causes of industries’ decline from the COVID-19 pandemic. In particular, re-loan policies should be implemented for the pharmaceutical products industry, medical instruments and equipment and most service sectors. Moreover, targeted VAT tax reductions should be implemented for agriculture, most manufacturing industries, hotels and dining, real estate and rental and business services sectors. In addition, an integrated policy could jointly expand the output-pulling effects of fiscal and monetary policies, as the effect of the integrated policy is more pronounced for secondary industries than the single policy. The reasons for this are twofold. First, secondary industries have a high proportion of intermediate inputs and low value-added products; thus, the VAT reduction will have a greater effect on reducing production costs. Second, the capital demand of the secondary industry is relatively large; thus, the reduction of LPR can reduce enterprises’ investment costs and increase the value of collateral assets, expanding production scale.

Fig. 4.

Output changes compared to the COVID-19 shock scenario.

5.3. The trade-off effects of recovery policies: GDP vs. CO₂ emissions

The recovery policies promoting economic growth have also led to a ‘reversal’ of carbon emissions reductions from COVID-19 restrictions. Table 3 demonstrates that carbon emissions increased after implementation of the response policies. In particular, the three VAT reductions increased CO₂ emissions by 4.22%, 3.40% and 0.74%, respectively, relative to the no-policy scenario, while the three LPR reductions increase CO₂ emissions by 0.26%, 0.29% and 0.32%, respectively. Carbon emissions increase by 4.49% with the combined VAT-All and LPR-10bp scenario. While the above policies can effectively mitigate the macro-economic damage of the pandemic, different policies have different efficiencies in achieving the dual goals of economic recovery and carbon emissions reduction, requiring a trade-off between GDP and carbon emissions. Fig. 5 indicates the synergies between carbon emissions and GDP growth rates under the seven policies. Carbon emissions are positively correlated with economic growth. The slope of the scatter to the origin equals the growth rate of carbon emissions/GDP growth rate, i.e. elasticity, in which the smaller the slope, the smaller the percentage increase in carbon emissions corresponding to each 1% growth in GDP.

Fig. 5.

GDP and carbon emissions growth rates under the different scenarios.

We find that the carbon emissions growth rate from all seven policies is greater than the GDP growth rate and the carbon emissions growth from a single stimulus policy is greater than the GDP growth rate. However, from the perspective of simultaneously achieving economic recovery and China’s dual carbon goals, the LPR reduction policy is superior to the VAT cut policy in terms of economic and environmental synergies, resulting in the lowest increase in CO₂ emissions for the same increase in GDP. Scenario LRP-30bp elicits the greenest efficiency, whereas policy VAT-All is the least conducive to China’s emissions reduction targets. This is because fiscal policy has a stronger direct stimulus effect on the production sector, particularly output pulls for the more carbon-emitting mining and power sectors; thus, tax cuts contribute to increasing carbon emissions tremendously. Nevertheless, the effect of tax reduction policies depends on the position of the industry in the current economic structure and the dominant industry during the COVID-19 pandemic is not necessarily low-carbon, especially since industrial and energy consumption structures will change during COVID-19, such as reducing investment and clean energy use; therefore, it is necessary to implement structural tax reduction policies. In contrast, monetary policy stimulates more aggregate demand and cross-period substitution to further increase consumption levels and more relevant services with low-carbon intensity, resulting in relatively lower carbon emissions. However, increasing the intensity of monetary policy results in limited GDP growth. The efficiency of the integrated policy is superior to the single policy, and the carbon intensity per unit of GDP in the VAT-LPR scenario is lower than the VAT tax reduction policy, suggesting that the integrated policy can overcome the carbon emissions drawback of the VAT tax reduction policy. The GDP growth rate of VAT-LPR is greater than the sum of VAT-All and LPR-10bp scenarios, indicating that the integrated policy is more efficient for boosting the economy than the single policy. Although the integrated policy can supplement the shortcomings of each policy alone, the combination and targets of the integrated policy must be carefully formulated, and more policy space remains to strategically achieve the economic recovery and emissions reduction goals.

Stimulus policies can mitigate damage across sectors from the COVID-19 pandemic but weighing the efficiency of different policies in achieving the dual objectives (economic recovery and carbon emissions reduction) must also be accomplished across sectors. Fig. 6 plots the CO₂ emissions-output elasticities, i.e. the percentage change in CO₂ emissions caused by a 1% change in output for the major carbon-emitting sectors under the seven stimulus policies. Monetary policy has significantly smaller CO₂ emissions-output elasticities for almost all sectors than fiscal and combined policies. Among them, scenario LPR-30bp has the highest green efficiency in raising output, particularly in light industry, equipment manufacturing and energy production and supply, relative to fiscal policy. For agriculture, policies VAT-I, VAT-S and VAT-All produce larger carbon emissions when increasing output in the agricultural sector. For coal, petroleum and gas products, non-metal product manufacturing and metal smelting and product manufacturing sectors, the elasticities under these scenarios are less than 1, indicating that LPR reduction policy can boost the output of these service sectors with lower carbon emissions. Electricity production and supply has the highest carbon emissions, and the VAT-I scenario has the highest carbon emissions per unit of output, indicating that the tax cuts for the industrial sector contribute to the increase in carbon emissions in the electricity sector. For transportation, warehouse and post, the carbon emissions per unit of output are more than 1, indicating that the VAT reduction policy increases carbon emissions.

Fig. 6.

Carbon emission-output elasticity of major carbon-emitting sectors under policy scenarios.

6. Conclusion

The COVID-19 pandemic that swept the world had a substantially negative impact on the global economy. The pandemic and associated public health and recovery policies led to significant changes in energy consumption and CO₂ emissions. China has implemented various measures to promote economic recovery in response to the economic damage from COVID-19; however, the economic stimulus has inevitably led to increased carbon emissions. The Chinese government is under pressure to recover simultaneously from COVID-19 and reduce carbon emissions. Choosing an evidence-based green economic recovery policy is a reliable approach for achieving China’s sustainable development. This paper measured the impact of the COVID-19 pandemic on China’s key macro-economic indicators and CO₂ emissions using a CGE model to simulate the mitigating effects of three fiscal policies, three monetary policies and a combination of policies on the economy and trade-offs between different policies under the dual targets of boosting the economy and achieving reductions in carbon emissions. The main conclusions are threefold.

(1) The COVID-19 pandemic resulted in a 4.81% decrease in China’s GDP and a 4.58% decrease in CO₂ emissions compared to the pre-pandemic period. The emissions reduction rate is within the scope of the study by Shan et al. (2021), which determines that CO₂ emissions would be reduced by 3.9%–5.6% over five years (2020–2024) compared to the baseline scenario without COVID-19, although they applied an input–output method. The pandemic caused both supply- and demand-side shocks, further exacerbating the negative impact on the economy. The production-based and demand-related sectors were impacted, with carbon emissions in the recreation and public services, production and living services, transportation and energy production and supply in the basic sectors declining by more than 5%.

(2) Recovery policies effectively mitigate macro-economic damage, and the VAT-LPR scenario, combining fiscal and monetary policies has the most substantial effect on the economy. In terms of stabilising aggregate consumption, reducing LPR is superior to the VAT reduction policy because VAT cuts have a constricting effect on residential consumption and reduce government revenue and purchasing power. Conversely, VAT cuts outperform LPR reductions for investment and international trade. VAT cuts are more direct in expanding profits in the productive sector and are suitable for managing supply-side shocks and export-dependent economies. Price increases resulting from LPR reductions may diminish the boost to export-oriented industries but can mitigate the damage of demand-side shocks. This finding is consistent with the conclusion of Liu et al. (2021b) that both monetary and fiscal policy responses are effective in mitigating economic damage to GDP; however, each approach has adverse side effects.

(3) The efficiency of different policies in achieving the dual targets of economic recovery and carbon emissions reduction varies. LPR reductions and integrated policies outperform VAT cut policies in terms of synergy for stimulating economic recovery and carbon emissions reduction goals across industries. Among fiscal policies, Scenario VAT-I is better than VAT-ALL. In practice, China has adopted a combination of proactive fiscal policy and prudent monetary policy to navigate economic shocks of COVID-19, and whether the economy can achieve a green recovery depends on the destination of the liquidity released and attending commitments to environmental constraints. The effects of COVID-19 in reducing economic aggregates may also cause structural changes in the economy. If, for example, economic recovery relies on stimulating carbon-intensive and traditional fossil fuel-based industries, it will prompt further energy dependence in production and consumption, with a greater lock-in effect on national carbon emissions, which is not conducive to the green upgrade of China’s economic structure after the pandemic.

As a result of our findings, we propose the following policy recommendations. The government should promote the interaction between economic restructuring and carbon emissions reduction using both fiscal and monetary policies. Emphasis on the use of integrated policies responds to the impact of COVID-19 so that the advantages and shortcomings of the two economic recovery policies complement one another, but it is necessary to carefully regulate the aggregate and structural nature of fiscal and monetary policies. Fiscal policy has the advantage of stimulating the supply side, causing a stronger effect of increasing carbon emissions. The government should target fiscal support towards the industries suffering the most severe damage from the pandemic, directing funds to resource-intensive manufacturing, scientific research and technical services, ecological and environmental protection, electricity and gas, transportation and other affected industries. Furthermore, compensation and government investment in clean energy and advanced technology-related industries should be increased to promote supply-side structural upgrading. In contrast, monetary policy can sufficiently manage demand shocks and stabilise investment and consumption with high green efficiency for different sectors. It can expand the scale of new loans, reduce total social financing costs and address the financing challenges of most market entities, particularly small, medium and micro-enterprises, to stabilise the economy. Consequently, under reasonable liquidity, government implementation and coverage of prudent monetary policy for economic stimulation should be increased, and the monetary policy transmission mechanism should be further unblocked. According to Central Government of China (GOV, 2022b), the Chinese government’s fiscal and monetary policies were upgraded in June, with efforts to relieve market participants and boost investment and consumption becoming essential. In fiscal policy, an additional 142 billion yuan is expected to be added to the original plan of 1.5 trillion yuan of tax rebates, with the total amount of new tax rebates reaching about 1.64 trillion yuan. It covers seven industries: wholesale and retail, agriculture, forestry, animal husbandry and fishery, accommodation, and catering. In terms of monetary policy, the percentage of incentive funds provided by the central bank through the inclusive micro and loan support instruments was increased from 1% to 2% from the second quarter of 2022. “To further mobilise the enthusiasm of financial institutions. In short, in the face of the current super-expected economic changes, macroeconomic policy stabilisation, intensification, and lean forwards are necessary to hedge against the negative impact of downward pressure on the economy. Fiscal and monetary incremental policies will create synergy to “accomplish a great task with little effort by clever manoeuvres” to accelerate economic stabilisation and recovery. In addition, the government should structurally guide funds to support the development of new green and low-carbon industries and the carbon-reducing transformation of traditional coal and other traditional energy industries.

This paper enriches the research on the macro-economic and environmental impacts of the COVID-19 and policy stimuli. It innovatively focuses on the efficiency and trade-offs of macro policies in economic recovery and carbon reduction targets, providing a theoretical basis and direct empirical evidence for policymakers to understand the costs of COVID-19 and response measures. Nevertheless, limitations remain. First, the paper does not consider trade and transportation restrictions imposed by other countries and may thus underestimate economic losses and carbon emissions reductions under COVID-19. Second, we only consider carbon emissions reductions in the production sector and do not measure the consumption sector; thus, our results do not include changes in consumer behaviour patterns. However, this may provide space and ideas for future research, such as investigating the impact of the COVID-19 and response policies on the economy and environment in the open economy model. The economic and social consequences of COVID-19 shocks are extremely complex, and multiple perspectives are worthy of future exploration. In addition to this paper’s focus on macro-economic and environmental impacts, attention must also be paid to the changes in social welfare and inequality triggered by the COVID-19 pandemic.

CRediT authorship contribution statement

Shiqi Jiang: Data collection and analysis, Methodology results discussion, Writing – original draft. Xinyue Lin: Data collection and analysis, Methodology results discussion, Writing – original draft. Lingli Qi: Data collection and analysis, Methodology results discussion, Writing – original draft. Yongqiang Zhang: Data analysis, Methodology results discussion, Funding acquisition. Basil Sharp: Supervision, Writing – reviewing and editing.

Appendix A. Sectors in the CGE model

Num.	Sector
Sec1	Agriculture
Sec2	Coal, petroleum and gas products
Sec3	Food and tobacco processed
Sec4	Textiles, clothes, shoes and leather
Sec5	Wood products and furniture
Sec6	Paper, printing and cultural product
Sec7	Chemicals manufacturing
Sec8	Pharmaceuticals manufacturing
Sec9	Non-metal product manufacturing
Sec10	Metal smelting and product manufacturing
Sec11	Machinery and equipment manufacturing
Sec12	Medical equipment manufacturing
Sec13	Waste recycling and disposal
Sec14	Electricity production and supply
Sec15	Gas production and supply
Sec16	Water production and supply
Sec17	Construction
Sec18	Wholesale and retail trade
Sec19	Transportation, warehouse and post
Sec20	Hotels and dining
Sec21	Communication, information and computer technology services
Sec22	Finance and insurance
Sec23	Real estate
Sec24	Lease and business services
Sec25	Scientific research and professional technology service
Sec26	Water and environment infrastructure service
Sec27	Residential and other services
Sec28	Education
Sec29	Health care
Sec30	Social work
Sec31	Cultural, sporting and recreational activity and service
Sec32	Public administration, social security and organisation

Appendix B. CGE model equations

Production module

Production function for goods

$$Q{A}_i={\lambda }_a{\left[{\mu }_{a}QV{A}^{\frac{{\sigma }_1-1}{{\sigma }_1}-}+\left(1-{\mu }_a\right)QINT{A}^{\frac{{\sigma }_1-1}{{\sigma }_1}}\right]}^{\frac{{\sigma }_1}{{\sigma }_1-1}}$$ (B.1)

Zero profit condition

$$P{A}_i\ast Q{A}_i=QV{A}_i\ast PV{A}_i+QINT{A}_i\ast PIN{T}_i$$ (B.2)

Production function for intermediate inputs

$$QIN{T}_{ij}=i{o}_{ij}\ast QINT{A}_i$$ (B.3)

Zero profit condition

$$PINT{A}_{ij}=\sum i{o}_{ij}\ast P{Q}_i$$ (B.4)

Value-added function

$$QV{A}_i={\lambda }_{va}{\left[{\mu }_{va}Q{K}^{\frac{{\sigma }_2-1}{{\sigma }_2}-}+\left(1-{\mu }_{va}\right)Q{L}^{\frac{{\sigma }_2-1}{{\sigma }_2}}\right]}^{\frac{{\sigma }_2}{{\sigma }_2-1}}$$ (B.5)

Zero profit condition

$$PL{K}_i\ast QL{K}_i=L_i\ast w_i+K_i\ast r_i$$ (B.6)

Income and expenditure module

Household

Household income

$$YH=\mathit{PL\; \ast \; QLS}+\mathit{PK\; \ast \; QKS}+\mathit{trans}{f_{gov}}_h$$ (B.7)

Household expenditure

$$EH=shr{h}_c\ast \left(1-t_h\right)\ast YH$$ (B.8)

Household saving:

$$HS=(1-shr{h}_c)\ast \left(1-t_h\right)\ast YH$$ (B.9)

Government

Government revenue:

$$YG=PT+DVAT+TARF+IMCT+\mathit{IMV\; CT}+DCT+DT$$ (B.10)

Government expenditure for commodities demand:

$$P{Q}_c\ast Q{G}_c=shr{g}_c\ast \left(YG-transf{r}_{g_h}\right)$$ (B.11)

Government saving:

$$GS=(1-shr{g}_c)\ast \left(YG-transf{r}_{g_h}-\mathit{transf}{r_g}_n\right)$$ (B.12)

Trade module

Armington function between imports and domestic goods

$$Q{Q}_c={\alpha }_c^q{\left[{\delta }_c^{q}Q{D}_c^{{\rho }_c^q}+\left(1-{\delta }_c^q\right)Q{M}_c^{{\rho }_c^q}\right]}^{\frac{1}{{\rho }_c^q}}$$ (B.13)

CET function between exports and domestic goods

$$Q{A}_a={\alpha }_a^t{\left[{\delta }_a^{t}QD{A}_a^{{\rho }_a^t}+\left(1-{\delta }_a^t\right)Q{E}_a^{{\rho }_a^t}\right]}^{\frac{1}{{\rho }_a^t}}$$ (B.14)

Wage curve:

$$P{Q}_c\cdot Q{Q}_c=P{D}_c\cdot Q{D}_c+P{M}_c\cdot Q{M}_c$$ (B.15)

$$P{A}_a\cdot Q{A}_a=PD{A}_a\cdot QD{A}_a+P{E}_a\cdot Q{E}_a$$ (B.16)

$$P{M}_c=pw{m}_c\cdot \left(1+t{m}_c\right)\cdot EXR$$ (B.17)

$$P{E}_a=pw{e}_a\cdot EXR$$ (B.18)

Closure rule:

$$\sum _{c}pw{e}_c\cdot Q{E}_c=\sum _{c}pw{m}_c\cdot Q{M}_c+FINV$$ (B.19)

Carbon module

$$EnCO{2}_a=QXE{N}_a\cdot em{f}_q$$ (B.20)

Market balance

Goods and services market balance

$$Q{Q}_c=\sum _{a}QIN{T}_{ca}+Q{H}_c+Q{G}_c+QIN{V}_c$$ (B.21)

Saving/investment balance

$$EINV=\sum _{c}P{Q}_c\cdot QIN{V}_c+EXR\cdot FINV$$ (B.22)

$$EINV=HSAV+GSAV$$ (B.23)

Endogenous and exogenous variables

Variables	Descriptions	Variables	Descriptions
$Q{A}_i$	Domestic output	$Q{M}_a$	Import
$QV{A}_i$	Capital and labour input	$Q{E}_a$	Export
$QINT{A}_i$	Intermediate input	$P{E}_a$	Price of export
$QIN{T}_{ij}$	Intermediate input matrix	$P{M}_c$	Price of import
$PINT{A}_{ij}$	Price of intermediate	$EXR$	Exchange rate
$K_i$	Capital demand	$FINV$	Foreign investment
$L_i$	Labour supply	$EnCO{2}_a$	Carbon emission
$r_i$	Capital price	$QXE{N}_a$	Energy consumption
$w_i$	Wage rate	${\lambda }_a$	Scaling parameter of CES production function
$YH$	Household income	${\lambda }_{va}$	Scaling parameter of CES function of inputs
$EH$	Household consumption	${\alpha }_c^q$	Scaling parameter of Armington function
$HS$	Household saving	${\alpha }_a^t$	Scaling parameter of transformation function
$\mathit{trans}{f_{gov}}_h$	Government transfers	${\mu }_a$	Substitution rate of intermediate input
$YG$	Government income	${\mu }_{va}$	Substitution rate of value-added input
$Q{G}_c$	Government consumption	${\sigma }_1$	Substitution elasticity of production
$GS$	Government savings	${\sigma }_2$	Substitution elasticity of value added
$PT$	Production tax	$\delta \left(CC\right)$	Substitution rate of Armington assumption
$DVAT$	Domestic VAT	$\varepsilon \left(CC\right)$	Substitution rate of transformation assumption
$TARF$	Import tariffs	${\rho }_c^q$	Substitution elasticity of Armington
$IMCT$	Income from consumption tax on imported commodities	${\rho }_a^t$	Substitution elasticity of CET
$IMVAT$	Income from VAT on imported goods	$i{o}_{ij}$	Table of intermediate inputs or uses
$DCT$	Income from consumption tax on domestic goods	$shr{h}_c$	Composition of household consumption
$DT$	Individual income tax	$t_h$	Proportion of household transfer to government
$Q{Q}_c$	Quantity of composite commodity supplied to or consumed in the domestic market	$transf{r}_{g_h}$	Proportion of government transfer to household
$Q{D}_c$	Quantity of domestically-produced commodity sold in the domestic market	$shr{g}_c$	Composition of government consumption
$Q{A}_a$	Quantity of domestically-produced commodity	$em{f}_q$	Carbon emissions coefficient