## Recalibrating the COVID Strategy (excerpt) — 1chnea2023002

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---

### Introduction and context
- Zero-COVID strategy (ZCS) relied on non-pharmaceutical interventions (NPIs): rapid lockdowns, mass testing, contact tracing, quarantine at government facilities, largely closed borders.
- Stages of policy evolution:
  - Stage 1: Emergency containment (Wuhan).
  - Stage 2: Prevention of imports and domestic rebound through expanded testing.
  - Stage 3: "Dynamic clearing" — more rapid and precise responses (Delta).
  - Stage 4: "Scientific and precise dynamic clearing" — all-round comprehensive prevention and control.
  - Stage 5: "20 measures" and ongoing easing of restrictions (late-2022).
- Late-2022 easing highlights:
  - November 2022 “20 measures” followed by allowing home quarantine, reducing mass testing, eliminating testing requirements to enter public places and transportation, and lifting requirements for health codes for inter-regional travel.
  - Since October 2022 outbreaks became larger and more widespread; cases surpassed earlier waves even before testing was rolled back.

### Economic impact and behavior elasticities
- Cumulative output losses (actual outturns vs January 2020 WEO forecast):
  - 1.5 percent of GDP over 2020 and 2021.
  - More than 4½ percent by end-2022.
- Private consumption losses:
  - More than 5 percent over 2020 and 2021.
  - Estimated to have increased to almost 13 percent by end-2022.
- Supply of services relative to pre-crisis trend:
  - Almost 4 percent below trend in 2020-21.
  - More than 8 percent below trend by Q3-2022.
- Private consumption-to-GDP share:
  - Fell to around 35 percent from its pre-pandemic share of around 37.
- Empirical elasticities (panel regression, 95 major cities, January 2020–July 2022):
  - A one percent drop in mobility → 0.66 percentage point decline in GDP on average since 2020 (Mobility coefficient on GDP: 0.66*** (0.53, 0.80)).
  - Retail sales: one percent drop in mobility associated with retail sales being 1.16 percentage points lower (Mobility coefficient on Retail sales: 1.16*** (0.96, 1.35)).
  - Disposable income: one percent drop in mobility → 0.37 percentage point decline in disposable income (Mobility coefficient on Disposable income: 0.37*** (0.13, 0.61)).
  - Additional amplification from NPIs: Delta and Omicron waves associated with further declines in retail sales and disposable income (Retail sales Delta -3.84*** (-6.04, -1.65); Retail sales Omicron -7.24*** (-9.31, -5.18); Disposable income Delta -2.99** (-5.79, -0.20); Disposable income Omicron -4.46*** (-7.21, -1.71)).
- Voluntary mobility responses:
  - Voluntary mobility decreased in response to rising cases and deaths overall.
  - Voluntary mobility reacted negatively to the Delta variant but increased amid the Omicron variant.

### Health cost of a sudden lifting of all containment measures
- Risks of immediate withdrawal of all NPIs given:
  - Relatively lower vaccination for the most vulnerable, possible lack of antiviral treatments, limited medical capacities (notably ICU beds).
- Cai and others (2022) baseline scenario (maintaining vaccination pace as of March 1, 2022; 20 imported Omicron cases) over 6 months projects:
  - 112 million symptomatic cases,
  - 2.7 million ICU admissions,
  - 1.6 million deaths,
  - three quarters of deaths among unvaccinated individuals aged 60 and above.
- Countermeasures effects (Cai and others, 2022):
  - Full elderly vaccination (all eligible aged 60 and above vaccinated) → decreases of 33.8, 54.1 and 60.8 percent in hospitalization, ICU admissions and deaths, respectively, compared to baseline.
  - Using China’s approved antivirals at an 80 percent effective rate → decreases of 36.5, 39.9 and 40 percent in hospitalization, ICU admission and deaths, respectively.
  - Best-case (all cases treated by highly effective medicines) → decreases of 81.2, 88.8, and 88.9 percent in hospitalization, ICU admissions, and deaths, respectively.
- Without high elderly vaccination and antiviral treatments, only strict NPIs reducing reproduction number to ≤ 2 can meaningfully lower medical burden; strict NPIs may only delay epidemics beyond a 6-month horizon.

### Preparing for gradual lifting of restrictions — public health prerequisites
- Essential preconditions before full easing:
  - Filling the vaccination gap, especially among the most vulnerable, so infections may rise but severe cases remain low and hospital/ICU demand within capacity.
  - Make antiviral therapies widely available.
  - Scale up health-care capacity.
- Recommended interim measures (observed in other economies):
  - Continue safe distancing, mask wearing, home quarantine for infected/close contacts (replacing mandatory government quarantine), testing including rapid tests, targeted capacity restrictions.
  - Ease and eventually abandon contact tracing as appropriate.
- Communication and staging:
  - Well-communicated, staged transitions reduce voluntary social-distancing responses that amplify economic losses.

### China vaccination statistics and vaccine effectiveness
- As of November 2022:
  - 90 percent of the population have received at least two shots,
  - 63 percent have been boosted,
  - less than 6 percent of the population received a shot in the last six months.
- Over-80-year-old group:
  - Just over 65 percent have received two shots,
  - Around 40 percent have been boosted.
- Vaccine effectiveness evidence (Hong Kong SAR Omicron outbreak, McMenamin and others, 2022):
  - Three doses of Sinovac could be effective at preventing severe illness.
  - Both BioNTech and Sinovac offer very high levels of protection against severe outcomes following a booster shot (above 95 percent across all age groups).
- Logistics:
  - Boosting the entire population could be achieved within few months if the vaccination pace returned to the initial 2021 campaign pace, assuming no supply constraints and no vaccine hesitancy.
- Data gaps:
  - Impact of new variants and waning effectiveness; likely need for repeated booster campaigns.

### Policy recommendations (explicit)
- China should:
  - (i) adopt a well communicated strategy,
  - (ii) ramp up roll out of booster shots effective against new COVID variants and target the under-vaccinated elderly,
  - (iii) make antiviral therapies—domestically developed or sourced abroad—widely available,
  - (iv) scale up health-care capacity,
  - (v) gradually adjust containment measures to be more flexible and less restrictive for economic activity while containing transmission within healthcare capacity.

---

### Empirical strategy: Mobility, policy stringency, and economic activity

### Data and definitions
- Mobility: Gaode 100-city congestion index; expressed as percent deviation from average monthly mobility 2017–2019.
- City-level economic variables: nominal GDP, retail sales, disposable income; deviations from pre-COVID trend based on CAGR 2016–2019.
- Stringency index: Oxford COVID-19 Government Response Index (0–100).
- Confirmed cases and deaths: National Health Commission of China.
- Dominant variant assumptions: Delta for Q3/July 2021–Q4/February 2022; Omicron since Q1/March 2022.
- Voluntary mobility proxied by residual after accounting for policy stringency.
- Regressions include individual fixed effects and seasonality adjustments.

### Key regression estimates (selected)
- Mobility and economic outcomes (Table 1):
  - Mobility coefficient on GDP: 0.66*** (0.53, 0.80). Observations: GDP 896. R2: 0.58.
  - Mobility coefficient on Retail sales: 1.16*** (0.96, 1.35). Observations: Retail sales 815. R2: 0.38.
  - Mobility coefficient on Disposable income: 0.37*** (0.13, 0.61). Observations: Disposable income 566. R2: 0.47.
- Stringency and voluntary mobility (Table 2):
  - Stringency on GDP: -0.23*** (-0.31, -0.16). Observations: GDP 896. R2: 0.58.
  - Voluntary Mobility on Retail sales: 1.14*** (0.94, 1.34). Observations: Retail sales 815. R2: 0.38.
- Policy response determinants (Table 3):
  - Confirmed cases (log diff) on Stringency: 0.02*** (0.02, 0.03); on Voluntary mobility: -0.002*** (-0.004, -0.001).
  - Deaths (log diff) on Stringency: 0.05*** (0.03, 0.06); on Voluntary mobility: -0.02*** (-0.03, -0.02).
  - Delta on Stringency: 3.46*** (2.51, 4.41); on Voluntary mobility: -1.13*** (-1.60, -0.66).
  - Omicron on Stringency: 4.68*** (3.50, 5.86); on Voluntary mobility: 1.48*** (0.92, 2.04).

### Interpretations
- Both policy stringency and voluntary mobility shifts matter for economic outcomes.
- Delta and Omicron variants altered both policy responses and voluntary behavior, amplifying economic effects during the waves.

---

### Fiscal multipliers in China — approaches and model results

### Challenges and literature range
- Estimation challenges: dynamic endogeneity between government balance and aggregate demand.
- Short-term spending multipliers in China reported in literature between 0.3 –   1.7 (Text Table).
  - Examples: China ST 1.7 Consumption multiplier Wang and Wen (2013); China LT 1.6/1.0 Economic upturn/downturn Zhang, Zhang, Zheng, and Zhang (2019); China ST 0.5/0.6 Jeong, Kang, and Kim (2017); China ST 0.6 Guo, Liu, and Ma (2016); EMs ST 0.2 0.3 Ilzetzki (2011); China ST 0.3/1.1 0.4 G Ducanes and others (2006); Emerging Asia ST 1 0.5 Freedman and others (2009).
- Heterogeneity arises from sample periods, identification techniques, and instruments.

### “Bucket” approach summary
- Three-step process:
  1. Assess structural characteristics that influence multiplier.
  2. Assign to low/medium/high short-term multiplier bucket.
  3. Adjust for conjunctural situation (cycle and monetary stance).
- China structural metrics (2015-19 and other inputs):
  - Imports ≈ 17.5 percent of domestic demand (threshold < 30 percent → score 1).
  - Labor rigidity index ≈ 0.5 (threshold > 0.8 → score 0).
  - Public expenditure ≈ 33 percent of GDP (threshold < 40 percent → score 1).
  - AREAER classification: "other managed" / Crawl-like → score 1.
  - Debt ≈ 110 percent of GDP (threshold < 70 percent → score 0).
  - PEFA/Revenue admin: N/A → score 0.
  - Total score = 3 → placed in low multiplier bucket in this paper.
- Conjunctural adjustments:
  - "Normal times" multiplier (MNT) range: 0.0 – 0.3.
  - Cycle adjustment: Cycle ≈ 40 percent (0.4) for negative output gap exceeding 2 percent.
  - Monetary stance factor: Mon ≈ 20 percent (0.2).
- Final short-term overall multiplier potential range: 0.2 –   0.5.

### DSGE model and policy scenarios
- Two methodologies used: bucket approach and estimated New Keynesian DSGE model.
- DSGE model features:
  - Two household types: liquidity-constrained ("non-savers") and unconstrained ("savers").
  - Fiscal instruments: transfers, public investment, differentiated labor and capital taxes, consumption tax.
  - Share of liquidity-constrained households assumed ≈ 50 percent.
- Fiscal shock scenarios (calibrated):
  - Large negative demand shocks lower output 3 percent below potential.
  - Public debt-to-GDP calibrated to 81.6 percent (2019 level).
  - Three fiscal responses analyzed: means-tested transfers to lower-income households, untargeted transfers to all households, increase in public investment.
  - Fiscal package normalized to one percent of GDP in first year; phases out via AR(1).
  - Investment efficiency assumed = 0.75.
- Model multipliers (short-run, cumulative per yuan spent):
  - Means-tested (targeted) transfers: short-term multiplier estimate = 1.5.
  - Untargeted transfers: multiplier below 1.
  - Public investment: short-run output multiplier = 1.
- Policy implications from simulations:
  - Means-tested transfers most effective in closing short-term output gap when interest rates broadly unresponsive.
  - Recommend reprioritizing spending away from infrastructure investment toward spending that boosts private consumption (targeted direct income support).

---

### Monetary policy, credit policies, and firm-level investment responses

### Institutional context and recent shifts
- PBC framework increasingly emphasizes quantity-based (credit) tools over traditional interest-rate instruments in recent years.
- Credit tools include relending and rediscount facilities and industry-targeted relending facilities (notably expanded in April 2022).
- Potential drawbacks of quantity-based tools:
  - Weaker countercyclical impact, muted capacity to stimulate activity among smaller and younger firms, and potential to exacerbate credit allocation towards larger, lower-risk firms.

### Empirical approach (firm-level local projections)
- Identification:
  - Interest rate shocks: unexpected innovations proxied by changes in short-term interest rate swaps on policy announcement days (25 basis point unexpected decrease chosen, approx one standard deviation; standard deviation ≈ 23.3 bps).
  - Credit policy shocks: residuals from a quarterly equilibrium credit growth model; standardized as a 12.5 percentage point unexpected increase in credit above trend (approx one standard deviation; standard deviation ≈ 12.7 pp).
- Data:
  - Semi-annual firm-level data 2011–2021 from a 14,000-firm database (Capital IQ; WIND; Bloomberg).
  - Sample: 138,422 firm-period observations; N. firms = 13,130; about 95,000 observations are private firms.
- Baseline regression controls: firm fixed effects; lagged firm-level controls (revenue growth, investment, liquidity, leverage); Driscoll-Kraay standard errors.

### Key empirical results — size and age heterogeneity
- Interest rate shocks (unexpected 25 bps decrease):
  - Small firms (assets < RMB 330 million): +9.5 percentage points increase in annual investment growth at 1.5 years.
  - Large firms (assets > RMB 4.5 billion): +2.6 percentage points at 1.5 years.
  - Transmission is lagged, strongest at three periods (1.5 years) and fades thereafter.
- Credit policy shocks (unexpected 12.5 pp increase in credit above trend):
  - Peaks two periods (one year) later.
  - Small firms: investment growth increases by 3.6 percentage points one year ahead.
  - Differential benefits for small firms smaller than for interest rate shocks; estimates for credit policy effects often less precisely estimated.
- Firm-age findings:
  - Interest rate shocks: largest responses for young firms (<14 years).
  - Credit policy shocks: quantitatively larger impacts for older and middle-aged firms but not statistically significant across age segments.

### Ownership, financial constraints, and coordination
- Ownership-based heterogeneity:
  - Credit policy shocks statistically significant primarily for larger state-backed firms (Central SOEs and LGFVs), with significant responses at two-to-three periods ahead.
  - Evidence consistent with lack of competitive neutrality in credit markets.
- Financially constrained subgroup evidence:
  - Small, private, loss-making firms (sample 8,950 observations) show large, statistically significant responses to interest rate shocks at three periods ahead; responses to credit policy shocks smaller and insignificant.
- Coordination of policy tools:
  - Coordinated interest rate and credit policy shocks produce larger net investment responses; coordinated episodes are infrequent (five periods), limiting statistical strength.

### Robustness and detailed impulse responses (Appendix I highlights)
- Tables report horizon-by-horizon ∆Yi,t ... ∆Yi,t+4 estimates by firm size and age for both easing and tightening directions.
  - Example significant entries:
    - Interest rate shock, Small: ∆Yi,t+2 = -0.41*** (0.14) in one robustness table (reflecting short-run dynamics in specific specifications).
    - Credit policy shock, Large: ∆Yi,t+4 = 0.37*** (0.05).
- Observations per horizon range between ~55,000 and ~138,000 depending on specification and grouping.
- Confidence intervals shown are 90 percent in figures and tables.

### Interpretation and policy recommendations
- Interest rate policy:
  - More effective at benefiting smaller and younger firms’ investment growth—should remain primary monetary instrument for cyclical management.
  - Interest rate easing operates rapidly and helps limit scarring by reducing firm closures and employment losses.
  - Prudential safeguards needed to mitigate increased financial risk-taking from rate cuts.
- Credit policies:
  - Have limited broad-based credit channel effects; useful when market failures exist or to amplify interest rate shocks.
  - Prefer market-based, targeted instruments (e.g., targeted re-lending facilities with interest cost subsidies) over administrative loan growth mandates.
  - Use quantity-based tools sparingly and complementarily to interest rate policy to avoid arbitrage, misallocation, and adverse productivity consequences.

---

### Power sector reform and climate-finance summaries (selected diagnostics and recommendations)

### Power market reform: challenges and quantified benefits
- Problems with traditional system: low generation asset utilization, low energy efficiency, high pollutant emissions, renewable curtailment, regional protectionism.
- Economic dispatch and market-based pricing expected benefits:
  - Operational cost savings of about 11 percent per year in 2035 (IEA 2019).
  - Power sector carbon emissions fall by 15 percent under economic dispatch.
  - Optimal electricity prices under economic dispatch could be lower by about 5 percent compared to existing dispatch (Timilsina, Pang and Yang, 2021).
  - IEA modelling: integrate renewables at over 20 percent of total generation without curtailment given improved operations and interconnections.
  - IEA modelling: operational cost savings and CO2 reductions from flexible dispatch bring savings of USD 63 billion annually.
- Policy measures recommended:
  - Move from planned fair dispatch to economic dispatch, expand spot market trading, harmonize inter-provincial markets, incentivize grid investment and ancillary services, put in place capacity mechanisms and transparent pricing, and manage transition costs and distributional impacts.

### Green finance and transition finance diagnostics
- Empirical findings:
  - At least 60 percent of issuers obtained lower financing costs for green bonds compared with non-green bonds of comparable maturity.
  - Green bonds and green equities show positive cumulative excess returns since 2018.
  - Bond issuance by carbon-intensive sectors has been more limited; net bond issuances of these sectors turned negative in 2017 and 2021.
- Green finance composition (2022Q3 bank green lending allocation):
  - Infrastructure 45 percent; Clean energy 26 percent; Energy saving and environmental protection 14 percent; Other green investment 16 percent.
- Policy initiatives and instruments:
  - PBC Carbon Emission Reduction Facility (CERF) re-lending under CERF as of end-June 2022 = RMB 182.7 billion.
  - Re-lending scheme to support clean and efficient coal: current quota RMB 300 billion; as of June 2022 re-lending amounted to RMB 35.7 billion.
  - April 2021 Green Bond Endorsed Project Catalogue updated with “do no significant harm”; July 2022 China Green Bond Principles require proceeds used to finance green projects.
- Gaps and recommendations:
  - Strengthen climate information architecture, require mandatory disclosure of carbon emissions and exposures, improve data quality and external review, adopt unified economy-wide taxonomy and a transition taxonomy, make external review and post-issuance verification mandatory where appropriate, and condition policy support on climate disclosures and emissions targets.
  - Enhance supervisory capacity for climate-related risks, promote climate stress testing, ensure adequate capital buffers (Pillar 2 as needed), and avoid preferential risk weights without empirical evidence.

---

### Long-term growth scenarios and policy implications

### Baseline scenario (no significant structural reforms)
- Labor: UN medium fertility growth scenario; average retirement age of 54 remains constant.
- Investment: investment-to-GDP ratio falls by about 1 percentage point in the long term from its current level.
- TFP: within-sector TFP growth remains constant at current level.
- Potential growth projections:
  - About 4 percent on average between 2023-27.
  - About 3 percent on average over 2028-37.
- Historical comparators (last 10 years averages): 6 percent sustainable GDP growth; 7 percent actual GDP growth; 6 percent real per capita GDP growth.

### Upside scenario (phased reforms over 15 years from 2023)
- Key reform assumptions:
  - Investment-to-GDP ratio converges to AE average of 22 percent over 15 years.
  - Retirement age increases: females +10 years (from 55 to 65) and males +5 years (from 60 to 65) over long run.
  - Human capital converges to AE level within 15 years.
  - SOE reforms close SOE-POE productivity gap in secondary sector by ~6 percent by 2038; market-dynamism reforms boost secondary-sector productivity by 1 percentage point.
- Projected gains:
  - Average GDP growth ≈ 4.5 percent between 2023-37.
  - Real GDP level uplift ≈ 2.5 percent by 2027 relative to baseline; ≈ 18 percent by 2037 relative to baseline.
  - Higher consumption share of GDP by around 18 percentage points in 2037; consumption improves by 75 percent over same period.
  - Direct CO2 emissions reduction ≈ 15 percent by 2037.
  - Augmented public debt by 2037 falls from 173 percent of GDP under baseline to 146 percent in upside scenario.

### Policy implications
- Need comprehensive reforms: boost labor supply (retirement age reform), human capital, SOE and market-dynamism reforms, rebalance from investment-led to consumption-driven growth, reprioritize fiscal spending toward households (social protection) to reduce precautionary savings, and integrate climate objectives with structural reform.
- Risks without reform: continued aging and declining productivity suppressing long-term growth; additional downside risks include prolonged zero-COVID policies and geoeconomic fragmentation.

*Italic: Source — 1chnea2023002 (excerpted chapter content).*

### References _______________________________________________________________________________________ 15

### References / Recalibrating the COVID Strategy (excerpt)

### Introduction and context
- Zero-COVID strategy (ZCS) relied on non-pharmaceutical interventions (NPIs): rapid lockdowns, mass testing, contact tracing, quarantine at government facilities, largely closed borders.
- ZCS helped keep COVID cases, hospitalizations, and death rates very low by international comparison despite limited medical resources (example: China has 3.6 critical care beds per 100,000 population).
- Policy evolution (stages identified in text figure):
  - Stage 1: Emergency containment (Wuhan).
  - Stage 2: Prevention of imports and domestic rebound through expanded testing.
  - Stage 3: "Dynamic clearing" — more rapid and precise responses (Delta).
  - Stage 4: "Scientific and precise dynamic clearing" — all-round comprehensive prevention and control.
  - Stage 5: "20 measures" and ongoing easing of restrictions (late-2022).
- In late-2022 authorities started easing containment: November 2022 “20 measures”; subsequent easing included allowing home quarantine, reducing mass testing, eliminating testing requirements to enter public places and transportation, and lifting requirements for health codes for inter-regional travel.
- Since October 2022 outbreaks became larger and more widespread than earlier in the year, and cases surpassed earlier waves even before testing was rolled back.

### Economic impact and key statistics
- Cumulative output losses (actual outturns vs January 2020 WEO forecast):
  - 1.5 percent of GDP over 2020 and 2021.
  - More than 4½ percent by end-2022 (after significant disruptions throughout 2022).
- Private consumption losses:
  - More than 5 percent over 2020 and 2021.
  - Estimated to have increased to almost 13 percent by end-2022.
- Supply of services relative to pre-crisis trend:
  - Almost 4 percent below trend in 2020-21.
  - More than 8 percent below trend by Q3-2022.
- Private consumption-to-GDP share fell to around 35 percent from its pre-pandemic share of around 37.
- Empirical elasticities (panel regression on congestion data from 95 major cities, January 2020–July 2022):
  - A one percent drop in mobility led to a 2/3 percentage point decline in GDP on average since 2020.
  - Retail sales: every one percent drop in mobility associated with retail sales being 1.6 percentage points lower relative to pre-COVID trend.
  - Additional amplification due to tightening NPIs: further declines of 3.8 and 7.1 percentage points for the Delta and Omicron waves, respectively (reflecting Stage 3 and Stage 4 intensity).
  - Household disposable income: every one percent drop in mobility led to a 0.4 percent drop in disposable income per capita.
- Voluntary mobility response:
  - Voluntary mobility decreased in response to rising cases and deaths overall.
  - Voluntary mobility reacted negatively to the Delta variant but increased amid the Omicron variant (more infectious but less lethal), reflecting higher public confidence in vaccine protection and possible fatigue with repeated lockdowns.

### Lessons from other economies that removed ZCS-style policies
- Common features observed in Australia, New Zealand, Singapore, Vietnam, and others:
  - Trigger: Rising economic costs under less lethal but more transmissible variants motivated exit.
  - Communication: Lifting was often communicated well in advance, sometimes linked to vaccination goals.
  - Vaccinations: Removal or easing of NPIs was preceded or accompanied by very high vaccination rates (including boosters), especially for the elderly, and availability of treatment options and enhanced health care capacity.
  - Shifting goals: Policy focus shifted from keeping cases close to zero to accepting outbreaks; acceptable case levels depended on health care capacity and vaccination protection.
  - From strict lockdowns to targeted measures: Ending hard lockdowns before sequentially lifting other restrictions; some measures remained (masking, distancing, testing) and were adjusted by local conditions.
  - International reopening was facilitated by accepting domestic cases because imported cases were often small relative to local transmission.
  - Health consequences: Large waves of infection occurred after opening, but high vaccination levels lowered severity and healthcare systems were not overburdened in studied cases.
  - Economic impact: With less lethal variants, high vaccination rates, a prepared population, and gradual openings, short-term economic costs were mostly contained and consumption recovered despite outbreaks.

### Policy implications and recommended preparations (as stated)
- Full lifting of remaining restrictions should balance economic costs against still significant health risks.
- Prior to full lifting, urgent public health measures are recommended to contain health impact:
  - Increased vaccination coverage, with emphasis on elderly vaccination and boosters.
  - Better treatment options and enhanced health care capacity to mitigate severe outcomes.
- A prepared and orderly full reopening is likely to minimize the tradeoff between health consequences and economic costs; advance communication and staged transition measures help internalize change and reduce voluntary social-distancing responses that amplify economic losses.
- Transition measures observed elsewhere that can be used include continued safe distancing, mask wearing, home quarantine for infected/close contacts (replacing mandatory government quarantine), testing including rapid tests, and targeted capacity restrictions; contact tracing may be eased and eventually abandoned as appropriate.

_Excerpted from the chapter "Recalibrating the COVID Strategy" (References section and related material) of the provided IMF content unit._

### 3.0 and 4.5 percent contraction in  disposable income respectively.

### 1chnea2023002 - 3.0 and 4.5 percent contraction in  disposable income respectively.

### D. The Health Cost of a Sudden Lifting of All Containment Measures
- An immediate withdrawal of all non-pharmaceutical interventions could have severe health consequences given:
  - relatively lower level of vaccination for the most vulnerable,
  - possible lack of antiviral treatments,
  - still limited medical capacities (notably ICU beds).
- Cai and others (2022) baseline scenario (maintaining vaccination pace as of March 1, 2022; 20 imported Omicron cases) projects over a 6-month period:
  - 112 million symptomatic cases,
  - 2.7 million ICU admissions,
  - 1.6 million deaths,
  - three quarters of deaths among unvaccinated individuals aged 60 and above.
- Vaccination/antiviral effects from Cai and others (2022):
  - Closing the vaccination gap for the elderly (all eligible aged 60 and above vaccinated) would lead to a 33.8, 54.1 and 60.8 percent decrease in hospitalization, ICU admissions and deaths, respectively, compared to the baseline.
  - Using China’s approved antiviral therapies at an 80 percent effective rate could lead to a 36.5, 39.9 and 40 percent decrease in hospitalization, ICU admission and deaths, respectively.
  - Best-case scenario (all cases treated by highly effective medicines) could yield decreases of 81.2, 88.8, and 88.9 percent in hospitalization, ICU admissions, and deaths, respectively.
- Without high vaccination coverage among the vulnerable and antiviral treatments, only strict NPIs that reduce the reproduction number to values no larger than 2 can meaningfully lower medical burden; strict NPIs may only delay epidemics beyond a 6-month horizon.

### E. Preparing for the Gradual Lifting of Remaining Restrictions
- A prepared and orderly strategy to fully lift remaining restrictions can minimize the tradeoff between health outcomes and economic cost by:
  - moving away from the most stringent NPIs (e.g., lockdowns of those not infected; stringent mobility restrictions on vaccinated population),
  - relying more on protection from effective vaccines, treatments, and the existing healthcare system,
  - applying a phased withdrawal of social distancing measures.
- Essential precondition for full easing:
  - Filling the vaccination gap, especially for the most vulnerable, so that while infections may rise, high vaccination levels imply a high share of asymptomatic or mild cases isolatable at home, keeping severe cases and deaths low and hospital/ICU demand within capacity.

### China’s Vaccination Campaign — Key Statistics and Findings
- As of November 2022:
  - 90 percent of the population have received at least two shots,
  - 63 percent have been boosted,
  - less than 6 percent of the population received a shot in the last six months.
- Among the over-80-year-old group:
  - just over 65 percent have received two shots,
  - around 40 percent have been boosted.
- Vaccine effectiveness evidence (Hong Kong SAR Omicron outbreak, McMenamin and others, 2022):
  - Three doses of Sinovac’s domestic vaccine could be effective at preventing severe illness.
  - Both BioNTech and Sinovac found to offer very high levels of protection against severe outcomes following a booster shot (above 95 percent across all age groups).
- Boosting the entire population could be achieved within few months if the vaccination pace returned to the initial 2021 campaign pace, assuming no supply constraints and no vaccine hesitancy.
- Data gaps remain on the impact of new variants and waning of effectiveness; maintaining protection likely requires repeated booster campaigns.

### F. Conclusions — Policy Recommendations (explicitly stated)
- China should further adjust its COVID strategy to mitigate economic impact while protecting lives by:
  - (i) a well communicated strategy,
  - (ii) ramping up the roll out of booster shots that are effective against new COVID variants and targeting the under-vaccinated elderly,
  - (iii) making antiviral therapies—either domestically developed or sourced from abroad—widely available,
  - (iv) scaling up health-care capacity,
  - (v) gradually adjusting containment measures to be more flexible and less restrictive for economic activity while containing domestic transmission within healthcare capacity.

### Empirical Strategy and Results — Mobility, Policy Stringency, and Economic Activity
- Data and variables:
  - Mobility: Gaode 100-city congestion index; expressed as percent deviation from average monthly mobility 2017–2019.
  - City-level economic variables: nominal GDP, retail sales, disposable income; deviations from pre-COVID trend based on compounded annual growth rate 2016–2019.
  - Stringency index: Oxford COVID-19 Government Response Index (0–100, higher = stricter).
  - Confirmed cases and deaths: National Health Commission of China.
  - Dominant variant assumptions: Delta for Q3/July 2021–Q4/February 2022; Omicron since Q1/March 2022.
- Regression approach:
  - Panel regressions of percent deviations of economic variables from pre-COVID trend on mobility decline, adjusted for major virus variants and seasonality; mobility decomposed into policy stringency and voluntary mobility with residual used as voluntary component.
- Key empirical findings (Table estimates; 95 percent confidence intervals in parentheses):
  - Table 1 — Mobility and economic outcomes:
    - Mobility coefficient on GDP: 0.66*** (0.53, 0.80)
    - Mobility coefficient on Retail sales: 1.16*** (0.96, 1.35)
    - Mobility coefficient on Disposable income: 0.37*** (0.13, 0.61)
    - Delta variant effects: GDP 1.31* (-0.22, 2.84); Retail sales -3.84*** (-6.04, -1.65); Disposable income -2.99** (-5.79, -0.20)
    - Omicron variant effects: GDP 0.48 (-1.05, 2.01); Retail sales -7.24*** (-9.31, -5.18); Disposable income -4.46*** (-7.21, -1.71)
    - Observations: GDP 896; Retail sales 815; Disposable income 566. R2: 0.58, 0.38, 0.47 respectively.
  - Table 2 — Stringency, Voluntary Mobility, and economic outcomes:
    - Stringency on GDP: -0.23*** (-0.31, -0.16)
    - Stringency on Retail sales: -0.28*** (-0.39, -0.17)
    - Stringency on Disposable income: -0.31*** (-0.45, -0.18)
    - Voluntary Mobility on GDP: 0.63*** (0.49, 0.77)
    - Voluntary Mobility on Retail sales: 1.14*** (0.94, 1.34)
    - Voluntary Mobility on Disposable income: 0.30** (0.06, 0.54)
    - Delta and Omicron variant coefficients reported with mixed significance across dependent variables (see regression table).
    - Observations: GDP 896; Retail sales 815; Disposable income 566. R2: 0.58, 0.38, 0.48 respectively.
  - Table 3 — Policy response and voluntary mobility determinants:
    - confirmed cases (log diff) on Stringency: 0.02*** (0.02, 0.03); on Voluntary mobility: -0.002*** (-0.004, -0.001)
    - deaths (log diff) on Stringency: 0.05*** (0.03, 0.06); on Voluntary mobility: -0.02*** (-0.03, -0.02)
    - Delta on Stringency: 3.46*** (2.51, 4.41); on Voluntary mobility: -1.13*** (-1.60, -0.66)
    - Omicron on Stringency: 4.68*** (3.50, 5.86); on Voluntary mobility: 1.48*** (0.92, 2.04)
    - Observations: Stringency 2,779; Voluntary mobility 2,587. R2: 0.36 and 0.21 respectively.

### Additional Empirical Notes and Data Definitions
- Mobility benchmark: average monthly mobility 2017–2019 treated as pre-COVID levels; deviations measured since January 2020.
- Counterfactual economic benchmarks: extrapolated using pre-COVID trend growth (CAGR 2016–2019) for each city; actual turnouts transformed into percent deviation from counterfactual.
- Voluntary mobility proxied by residual after accounting for policy stringency.
- Regression specifications include individual fixed effects and seasonality adjustment.

*Italic: Source — 1chnea2023002 - 3.0 and 4.5 percent contraction in  disposable income respectively.*

### 3.      Estimation of fiscal multipliers is challenging. Changes in the government balance and its

### 3.      Estimation of fiscal multipliers is challenging. Changes in the government balance and its

### Challenges in estimating fiscal multipliers
- Dynamic endogeneity: changes in the government balance and its revenue and expenditure components affect aggregate demand, and changes in aggregate demand affect the government balance and its components.
- Example: A discretionary increase in income tax rates aimed at lowering a fiscal deficit reduces aggregate disposable income, which reduces aggregate consumption, induces layoffs and a fall in aggregate taxable income, and can translate into lower tax revenue that offsets the initial effort to increase tax revenue.

### How estimated multipliers vary
- Estimated multipliers depend on structural characteristics and conjunctural setting (Izquierdo and others (2019) review).
- Factors cited that influence government spending multipliers:
  - State of the economy: multipliers larger in recessions than in expansions (Auerbach and Gorodnichenko, 2012 and 2013; Riera-Crichton, Vegh, and Vuletin, 2015).
  - Exchange rate regime: multipliers larger under fixed regimes (Ilzetzki, Mendoza, and Vegh, 2013).
  - Degree of indebtedness: multipliers larger when debt is low (Ilzetzki, Mendoza, and Vegh, 2013; Huidrom et al., 2019).
  - Degree of accommodation of monetary policy: multipliers larger when monetary policy is loose and/or close to the zero lower bound (Christiano, Eichenbaum, and Rebelo, 2011; Coenen, Straub, and Trabandt, 2013).
  - Degree of openness: multipliers smaller in economies more open to trade (Ilzetzki, Mendoza, and Vegh, 2013; Gonzalez-Garcia, Lemus, and Mrkaic, 2013).
- Output effect of public investment falls when efficiency is low (Leeper, Walker, and Yang, 2010; Cavallo and Daude, 2011; Furceri and Li, 2017).

### Range of estimates for China
- Short-term spending multipliers in China range between 0.3 –   1.7 (Text Table).
- Examples from the Text Table (preserve original labels and numbers):
  - China ST 1.7 Consumption multiplier Wang and Wen (2013)
  - China LT 1.6/1.0 Economic upturn/downturn Zhang, Zhang, Zheng, and Zhang (2019)
  - China ST 0.5/0.6 Depending on sample period Jeong, Kang, and Kim (2017)
  - China ST 0.6 Based on annual data from China's prefectures Guo, Liu, and Ma (2016)
  - EMs ST 0.2 0.3 Panel, 17 EMs Ilzetzki (2011)
  - China ST 0.3/1.1 0.4 G Increase/Decrease Ducanes and others (2006)
  - Emerging Asia ST 1 0.5 Averages of expenditures (ex. transfers) and tax instruments Freedman and others (2009) based on GIMF
- Heterogeneity in estimates reflects differences in sample periods, identification techniques, and estimation approaches.

### Methodologies employed in this paper
- Two methodologies used to explore size of short-term multipliers in China:
  1. The “bucket” approach (Batini, Eyraud, and Weber (2014)) to derive an indicative range for the overall fiscal multiplier (range for the average multiplier).
  2. An estimated New Keynesian DSGE model with detailed fiscal specifications to model multipliers for various fiscal instruments in responses to recessionary shocks and account for fiscal–monetary interaction.

### The “bucket” approach — framework and steps
- Purpose: synthesize literature into a granular approach to develop a range estimate of a country’s overall multiplier (output response to a change in the fiscal balance generated by a discretionary fiscal policy choice).
- Three-step process:
  1. Assess structural characteristics that influence level of multiplier.
  2. Assign the country to a short-term multiplier bucket (low/medium/high) based on a score.
  3. Adjust the “normal times” multiplier for conjunctural situation (business cycle and monetary policy stance).

### China’s structural characteristics (Step one) — metrics and values
- Trade openness:
  - China’s imports over the five-year period from 2015-19 averaged about 18 percent of domestic demand.
  - Weighted-average value for G20 emerging market economies over the same period: 22 percent.
- Labor market rigidity:
  - Botero and others (2004) assess China to have an index value of about 0.5 on a scale of from 0 – 1.
  - Threshold for high labor market rigidity: 0.8.
- Automatic stabilizers:
  - Threshold where stabilizers are considered large: 40 percent.
  - China’s public expenditure relative to nominal GDP over 2015-19 averaged about 33 percent.
  - Weighted-average value for G20 emerging market economies over same period: 31 percent.
  - Direct taxes, particularly the personal income tax, are low, averaging about 1.1 percent over the last several years.
- Exchange rate regime:
  - IMF’s AREAER classifies China’s de facto exchange rate regime as “other managed” arrangement, effective March 3, 2022. A not less-than-fully-flexible exchange rate tends to enhance the magnitude of fiscal multipliers compared to a fully flexible one.
- Debt level:
  - China’s overall level of general government debt stands at around 110 percent of GDP.
  - Average debt-to-GDP ratio for emerging markets, excluding China, of 59 percent of GDP in 2021.
- Public expenditure management and revenue administration:
  - No PEFA assessment available; effectiveness not objectively measured in the text.

### China’s score and bucket assignment (Step two)
- Scoring rule: characteristic above threshold = score of “1”; else score of “0”; equal weight across characteristics.
- Structural characteristics table (as reported):
  - Low trade openness Imports < 30 percent of domestic demand 17.5 1
  - High labor market rigidity Index > 0.8 (Botero and others, 2004) 0.5 0
  - Small automatic stabilizers Spending < 40 percent of GDP 33 1
  - Fixed/Quasi FX regime AREAER classification Crawl-like 1
  - Low public debt level Debt < 70 percent of GDP 110 0
  - Strong PFM/Revenue admin PEFA/ Revenue "gaps" N/A 0
  - Total 3
- Interpretation:
  - China’s total score of “3” places it in either the low or medium short-term multiplier bucket per Batini, Eyraud, and Weber (2014).
  - This paper places China in the low multiplier bucket, consistent with other emerging markets.

### Adjusting for conjunctural situation (Step three)
- Business cycle adjustments:
  - “Normal times” multiplier decreased by up to 40 percent at cyclical peak, increased by up to 60 percent at cyclical trough.
- Monetary policy adjustments:
  - Monetary policy stance adjusts the “normal times” multiplier by up to 30 percent (e.g., increase by 30 percent if monetary policy at zero-lower bound).
- Simple equation summarizes conjunctural impact (expression referenced but not numerically reproduced in full text).

### China’s conjunctural assessment and final “bucket” range
- China estimated to be operating below potential and monetary policy somewhat accommodative during 2022-23.
- “Normal times” overall multiplier (MNT) range: between 0.0 – 0.3.
- Output gap: negative output gap exceeding two percent of potential GDP → cyclical adjustment factor (Cycle) ≈ 40 percent (0.4).
- Monetary stance factor (Mon) ≈ 20 percent (0.2).
- Combining structural and conjunctural factors yields a potential range for China’s short-term overall multiplier of 0.2 –   0.5 (Text Table).

### Other sources of multiplier variation noted
- Policy uncertainty (e.g., potential for repeated lockdowns under zero-COVID) likely lowers fiscal multipliers.
- Fiscal instrument composition:
  - Meta-analysis: average estimates for public spending on goods and services ≈ 1; public investment slightly higher than public consumption (April 2020 WEO).
  - The DSGE model is applied to simulate multiplier differences across instruments (next section).
- Green investment: may have higher multipliers.
- Targeted public support for liquidity-constrained households (higher marginal propensity to consume) can generate higher fiscal multipliers than transfers to other households (Jappelli and Pistaferri 2014; McKay and Reis 2016).

### DSGE model simulation — purpose and features
- Purpose: study relative implications of various discretionary fiscal policy shocks on output under current cyclical position for China; focus on short-term implications as a stabilization instrument.
- Scenarios: three policy scenarios analyzed to compare public investment and household support measures in stabilizing output and supporting balanced recovery.
- Model features:
  - Based on Traum and Yang (2015) and Leeper, Plante, and Traum (2010).
  - Two types of households: liquidity-constrained (“non-savers”) and unconstrained (“savers”).
    - Liquidity-constrained households consume all disposable income each period; unconstrained households are forward-looking with access to complete asset and capital markets.
  - Extensive set of fiscal instruments: transfers, public investment, differentiated labor income taxes, capital income taxes, and consumption tax.
  - Debt stabilization via non-distortionary lump-sum taxes to higher-income households.
  - Liquidity-constrained households have higher marginal propensity to consume, increasing short-run demand effects.
  - Central bank sets policy interest rate based on a Taylor rule; implicit government interest rate modeled depending on average maturity structure of sovereign debt (Veld et al. (2012)).

### Model calibration — key parameter choices
- Government shares: consumption, investment, and transfer shares relative to GDP estimated using official data for 2016–2019.
- Capital share in Cobb-Douglas production assumed to be 0.4 (literature ranges from 0.36 to 0.45). Share of public and private capital in production: about 0.14 and 0.26, respectively.
- Effective labor income tax rate:
  - For liquidity-unconstrained households: between 10 and 20 percent.
  - For constrained households: around 10 percent.
- Share of liquidity-constrained households:
  - Literature ranges: 25 to 40 percent for advanced economies; higher for emerging economies.
  - Xie and Jin (2015) show poorest half held 8 percent of total national wealth; much wealth in illiquid assets.
  - Assumed share for China in analysis: around 50 percent.

### Transition to model results
- The paper proceeds to present model results (section indicated as "Model Results" in source).

*Source: 1chnea2023002 - 3.      Estimation of fiscal multipliers is challenging. Changes in the government balance and its*

### 20.      To best depict the current state of the Chinese economy, a number of fiscal policy

### 1chnea2023002 - 20.      To best depict the current state of the Chinese economy, a number of fiscal policy

### Fiscal shock scenarios and calibration
- Large negative demand shocks (preferences shocks and investment-specific technology shocks) lower output, consumption, and private investment, leaving the pre-policy level of output three percent below potential output.
- The public debt to GDP ratio is calibrated to 81.6 percent, the level in 2019.
- Three fiscal policy scenarios are analyzed:
  - Increase in the means-tested transfer to lower income households (targeted transfers to liquidity constrained households).
  - Increase in the untargeted transfer to all households.
  - Increase in public investment.
- Fiscal expansion design:
  - The fiscal expansion is normalized to one percent of GDP in the first year, and then gradually phases out.
  - The fiscal impulses are assumed to phase out following an AR(1) process.
  - Monetary policy remains broadly unresponsive during the episode.
  - Fiscal expansion is assumed to be in the form of public investment and transfers to all households or to liquidity constrained households only.
  - Investment spending efficiency is assumed to be 0.75.
- Multipliers are estimated as the cumulative change of output or consumption for each yuan spent in the fiscal package over the same horizon.

### Fiscal multipliers and empirical results
- All three fiscal spending measures raise output, but multipliers differ significantly.
- Means-tested (targeted) transfers:
  - Most effective in closing the output gap in the short term, particularly when interest rates remain largely unchanged (limiting crowding-out effects).
  - Short-term multiplier estimate for means-tested transfers is 1.5.
  - Rationale: liquidity constrained households have a higher marginal propensity to consume.
- Untargeted transfers:
  - Generate a multiplier below 1 for the same fiscal cost, reflecting that unconstrained households save part of the transfers.
- Public investment:
  - Short-run output multiplier of 1.
  - While efficient public investment increases the stock of future public capital, the benefits take time to materialize; impact operates indirectly through income effects and is significantly lower in the short run than direct transfers, especially means-tested transfers.
- The estimate on means-tested transfers is within the range of government spending multiplier estimates for China in the academic literature, though it is significantly higher than the top range of 0.5 from the “bucket” approach; divergence reflects that the overall multiplier averages across fiscal instruments and some instruments can have quite low multipliers.

### Policy recommendations and implications
- Fiscal measures focused on targeted, means-tested household support promise a higher growth impact and help rebalance the economy.
- Authorities should reprioritize spending away from infrastructure investment and towards spending that boosts private consumption.
  - Example: means-tested direct income support to vulnerable households who have a higher propensity to consume.
- Expected benefits of reprioritization:
  - Lower high household savings.
  - Help rebalance the recovery.
  - Help close the negative output gap.

### Monetary policy and the role of credit policies (overview)
- China’s monetary framework features both price- and quantity-based instruments; recent evolution increased emphasis on quantity-based (credit) tools relative to traditional interest rate tools.
- Historical context:
  - Up until 2012, the PBC primarily relied on a quantity-based framework (window guidance, loan growth targets, strict deposit and loan rate regulations).
  - About a decade ago, the PBC began liberalizing interest rate regulations and implementing a price-based framework to maintain short-term risk-free rates within an interest rate corridor.
- Role and design of credit policies:
  - Credit growth targets have become more tailored to bank characteristics (capital and asset quality) and form part of the Macroprudential Assessment (MPA).
  - Since 2018, maintaining stable credit growth that matched nominal GDP growth is a key objective.
  - In the last five years, credit policies proliferated at borrower-segment level (micro and small enterprises, privately owned firms, advanced manufacturing), using instruments like PBC relending facilities or policy requirements (e.g., State Council financial inclusion lending growth requirements).
- Recent shifts and concerns:
  - PBC usage of price-based tools (interest rate adjustments) has become less frequent and smaller in magnitude compared with other emerging markets.
  - Growth in PBC relending and rediscount facilities, and the proliferation of industry-targeted relending facilities in April 2022, indicate increased use of credit policy tools.
  - PBC communications highlight credit policies’ ability to provide precisely targeted support to small firms and stabilize the macroeconomy while avoiding “flood-like” credit stimulus.
- Potential drawbacks of increased reliance on quantity-based tools:
  - May weaken monetary policy’s countercyclical impact and impose other macroeconomic costs.
  - Credit policy appears to have more muted capacity to stimulate activity among smaller and younger firms—those most financially constrained and cyclically vulnerable—potentially limiting effectiveness for countercyclical demand management.
  - Credit policy benefits are clearer for larger, more established firms, which could exacerbate disparities in credit availability and hinder medium-term productivity growth.
- Empirical approach preview:
  - The paper uses local projections to compare impacts of different monetary policy tools on firm-level investment.
  - Interest rate tools are identified using a monetary policy shock approach; credit policy interventions are identified via a novel technique quantifying credit growth unexplained by macroeconomic and financial factors.
  - Relative impacts of traditional monetary policy shocks and unexplained shocks to total credit are estimated across a cross-section of 14,000 firms.

*Source: IMF staff estimates.*

### 9.      Quantity-based tools with resemblance to those used in China have been deployed

### 9.      Quantity-based tools with resemblance to those used in China have been deployed

### Background on quantity-based tools
- Quantity-based policies (funding-for-lending schemes) were used by many advanced economy central banks after the Global Financial Crisis to stimulate bank lending, notably the ECB’s Targeted Long-Term Refinancing Operations (TLTRO) facilities.
- These facilities generally lowered banks’ funding costs in exchange for meeting quantitative credit supply goals and aimed to boost bank lending when balance sheet constraints weighed on credit supply and policy rate cuts were limited by the zero lower bound.
- The impact of quantity-based tools on financially constrained firms is ambiguous:
  - Banks induced to lend via subsidized funding may allocate extra lending in ways that do not favor higher-risk or more constrained borrowers.
  - If pre-intervention lending was constrained by capital constraints or underwriting standards, banks may prefer to allocate additional credit to lower-risk borrowers.
  - Emerging empirical literature finds mixed evidence: some studies suggest the ECB’s most recent TLTRO induced banks to lend without scaling up loan portfolio risk; other work finds a “flight to quality” response.

### Relevance for China
- Historically, quantity-based policies in China supported countercyclical investment by state-owned firms and large-scale infrastructure while minimizing crowding-out for private borrowers.
- Scope for that role has narrowed as economy-wide leverage has risen and productive state-owned projects have become scarcer.
- The chapter assesses empirically the relative effectiveness of interest rate and credit policy shocks for easing borrowing conditions for cyclically exposed, financially constrained firms.
- The analysis does not fully capture recent sector- and segment-level credit policies (financial inclusion, micro and small enterprise lending), but findings have implications for the effectiveness of such policies.

### Methodology and data
- Empirical approach:
  - Panel local projections (Jorda 2006) estimate impulse response functions of firm-level investment to policy shocks, following Cloyne (2018) and Durante et al (2021).
  - Firm groupings are used to explore cross-sectional heterogeneity in investment sensitivity to monetary shocks.
- Identification of shocks:
  - Interest rate shocks: unexpected innovations proxied by changes in short-term interest rate swaps on days of monetary policy announcements (aligned with Gertler and Karadi 2015; Mohanty and Kamber 2018; Das and Song 2022).
  - Credit policy shocks: residuals from a quarterly equilibrium credit growth model. The model explains deviations of total social financing (excluding government bonds and equity) from trend using:
    - Taylor rule-based expected interest rate (activity and inflation variables; activity = gap between proprietary current activity indicator and trend; inflation = NBS Consumer Price Index)
    - Policy interest rate shock (shock_IR)
  - The residual is interpreted as policy interventions to slow or accelerate total credit growth relative to counterfactual market-driven outcomes (hereafter “credit policy”).
- Properties and limitations of the credit policy shock:
  - Use of the interest rate shock as a control addresses endogeneity between interest rate shocks and credit growth.
  - Identified credit policy shocks include periods of tightening and loosening (e.g., deleveraging years before COVID).
  - Limitations: shifts in observed credit growth could reflect omitted variables (bank/firm leverage, external shocks), structural breaks, or sector/segment policies not captured; the proxy thus has limitations but is reasonable for direction and magnitude of credit policy shocks.
- Baseline econometric specification:
  - Left-hand variable: year-on-year growth in net property, plants and equipment for firm i at horizon h.
  - Regressors include shock_IR_t and shock_CP_t interacted with firm-group dummy D_g,i, one- and two-period lagged firm-level controls (revenue growth, investment, liquidity, leverage), one- and two-period lags of policy shocks, firm fixed effects, and Driscoll-Kraay standard errors to address spatial correlation, autocorrelation, and heteroscedasticity.
- Data:
  - Semi-annual firm-level data from a 14,000-firm database (Capital IQ; identification/classification from WIND and Bloomberg), covering 2011–2021.
  - Sample: 138,422 firm-period observations with complete data; about 95,000 observations are private firms; N. firms = 13,130; Obs. = 138,422.
  - Data skew: coverage biased toward dynamic, well-funded SMEs listed on specialized SME bourses; unbalanced panel with more observations after 2014.
  - Data cleaning: removed observations without complete measures for investment growth, size, age, leverage (debt to assets), liquidity (cash & equivalents to assets), and revenue growth; trimmed investment values below 1st and above 99th percentiles or outside bounds of -99 to 2000 percent.

### Empirical results — firm-size heterogeneity
- Interest rate shocks:
  - An unexpected 25 basis point decrease in the short-term interest rate swap (approximate one standard deviation shock) generates:
    - For the smallest third of firms (assets below RMB 330 million): a 9.5 percentage point increase in the annual growth rate of investment one and a half years later (controlling for firm-level variables and credit policy shock).
    - For the largest third of firms (assets above RMB 4.5 billion): an increase of about 2.6 percentage points one and a half years later (roughly four times smaller than small firms).
    - Midsize firms: smallest and statistically insignificant reaction.
  - Timing: transmission to investment is lagged, manifesting most strongly three periods (one and a half years) after the initial shock and fading thereafter.
- Credit policy shocks:
  - An unexpected credit policy easing that generates a 12.5 percentage point increase in credit relative to trend (roughly one standard deviation) peaks in its impact on investment growth two periods (one year) later.
  - For small firms: investment growth increases by 3.6 percentage points one year ahead.
    - This is about 1.5 times greater than the result for large firms but the differential is smaller than for interest rate shocks.
    - Small firm results are not statistically different from zero or from the credit policy effect on larger firms (less precisely estimated).
- Standardization note:
  - 25 bps and 12.5 percentage points are chosen because they closely approximate one standard deviation of their respective time series (23.3 and 12.7, respectively).

### Empirical results — firm-age heterogeneity
- Interest rate shocks:
  - Largest investment response for young firms (<14 years old), incrementally smaller responses for middle-aged (14–20) and old firms (>21).
  - Results are generally statistically significant at one and one and a half years after the shock, except for older firms.
  - Magnitudes are smaller than size-based groupings.
- Credit policy shocks:
  - Quantitatively largest impacts are estimated for older and middle-aged firms, but results are not statistically significant across any age segment.

### Key statistics and sample descriptors (as reported)
- Firm-level sample descriptors:
  - Obs. = 138,422 firm-period observations with complete data.
  - N. firms = 13,130.
  - Of these observations, about 95,000 are private firms.
- Investment response magnitudes:
  - Interest rate shock: 25 basis point unexpected decrease → small firms (assets < RMB 330 million) → +9.5 percentage points annual investment growth at 1.5 years; large firms (assets > RMB 4.5 billion) → +2.6 percentage points at 1.5 years.
  - Credit policy shock: 12.5 percentage point unexpected increase in credit above trend → small firms → +3.6 percentage points investment growth one year ahead.
- Standard deviations approximated by chosen shock sizes:
  - Interest rate shock series standard deviation ≈ 23.3 basis points (25 bps chosen).
  - Credit policy shock series standard deviation ≈ 12.7 percentage points (12.5 pp chosen).

### Implications
- Interest rate-based monetary policy appears more effective at benefiting smaller and younger firms’ investment growth than quantity-based credit policy shocks in the Chinese context studied.
- Quantity-based credit policy shocks have earlier but more muted and less precisely estimated differential benefits for small firms relative to large firms.
- The findings raise questions about the suitability of a monetary framework centered on quantity-based tools if the policy objective is to preferentially support financially constrained, cyclically exposed firms.
- Limitations that bear on policy interpretation:
  - The credit policy shock is a proxy and cannot fully capture sector- or segment-level credit policies.
  - Firm sample skews toward better-funded SMEs and excludes many micro and small enterprises by official definitions, potentially biasing measured responses.
  - The sample period is relatively short and does not include a large economic downturn except H1 2020.

*IMF staff analysis from chapter section: 9. Quantity-based tools with resemblance to those used in China have been deployed*

### 23.      In ownership-based groupings, credit  policy shocks are also statistically significant

### 1chnea2023002 - 23.      In ownership-based groupings, credit  policy shocks are also statistically significant

### Key empirical findings on firm responses
- Ownership-based heterogeneity
  - Credit policy shocks are statistically significant only for larger state-backed firms.
  - Central SOEs’ investment growth response: correct sign and statistically significant at three periods ahead.
  - LGFVs (entities classified by the bond market regulator as local government financing vehicles): large and significant responses to credit policy shocks two periods ahead.
  - Consistent with prior IMF staff findings that SOEs and private firms do not face competitive neutrality in credit markets.

- Robustness of results
  - Controlling for the sign of the policy shock: results for small and young firms one and a half years ahead show the correct sign during both interest rate tightening and loosening; only results for small firms are statistically significant.
  - For credit policy shocks, only large, old, and middle-aged firms are correctly signed in both directions.
  - Findings hold when controlling for different asset thresholds for size-based groupings and variance in firm count across periods.
  - Similar results when restricting the firm sample to firms captured in 12 or more periods, and when excluding the COVID-period results.
  - Note: restricting results to firms with results in all periods excludes over 90 percent of firms with less than RMB 330 mn in assets. The threshold of 12 periods approximates the median reporting frequency for the smallest and youngest terciles of firms.

- Financially constrained subgroup evidence
  - First grouping: small (assets below RMB 330 million), private, loss-making firms with average negative cash flows over three periods (negative median investment growth throughout sample).
    - Sample size: 8,950 observations.
    - These firms exhibit large and statistically significant responses to interest rate policy shocks at three periods ahead.
    - Response to credit policy shocks: smaller and statistically insignificant.
  - Second grouping: firms in the top third for ratio of revenue-to-fixed assets (productivity proxy) but with average investment growth <10% over the sample.
    - Impulse response for this group: larger and more statistically significant for interest rate shocks; credit policy response notably larger and closer to being significant.
  - In general: top-third productivity proxy firms show large and statistically significant responses to interest rate shocks; bottom tercile firms see a statistically significant reaction but with the opposite sign.
  - Credit policy shocks are statistically insignificant across all productivity-based firm groupings.

- Coordination of policy tools
  - Replacing firm-group dummy with time-based categorical Dcoord,t where Dcoord,t = 1 when policy rate and credit policy shocks have the same sign; = 0 when they counteract.
  - Uncoordinated shocks (n=88,619): both shocks have the correct sign and similar magnitude, suggesting they largely offset each other.
  - Coordinated shocks (n=40,042): net investment response across the two shocks (sum of coefficients) is large and with the correct sign, reflecting a large contribution from the interest rate shock.
  - Policy coordination observed in only five periods, marginally weakening statistical strength of coordinated result.
  - Credit policy shock impact is wrongly signed towards the end of the projection period due to relatively larger magnitude of credit policy easing in one coordinated easing period where investment later weakened.

- Definitions of shocks and confidence
  - Each time period t is one half-year.
  - Interest rate shocks: unexpected 25 basis point decreases to the 1-year interest rate swap rate on days of monetary policy announcements.
  - Credit policy shocks: unexpected 12.5 percentage point increases in credit above trend levels.
  - Confidence intervals shown are 90 percent.

### Policy implications and mechanisms
- Limited effectiveness of credit policies in generating broad “credit channel” effects
  - Potential explanations (non-mutually exclusive):
    - Bank risk aversion: prudential requirements, capital constraints, or risk aversion may lead banks to allocate policy-induced lending to lower risk firms or assets (mortgages, government bonds).
    - Limited spillovers to riskier credit markets: segmentation and insufficient risk-adjusted returns for higher-risk borrowers lead additional credit supply to low-risk borrowers without benefiting high-risk borrowers.
    - Lack of coordination with interest rate policy: increasing money supply without complementary interest rate adjustment can limit demand stimulation.

- Why credit policies may be less effective for cyclical management
  - Indirect and uncertain transmission to most cyclical sectors: larger low-risk firms unlikely to fund productive investments; spillovers depend on supply-chain proximity and willingness to invest in less useful projects.
  - Credit channel weakens as cyclical stress worsens: banks increase risk aversion and allocate credit to least risky borrowers; demand from market-oriented borrowers declines.

- Adaptive and distributional effects on lending and financial conditions
  - Firms benefiting from credit policies (or proximate to them) may gain structurally easier external financing as investors anticipate policy support.
  - Creditors may charge higher risk premiums to higher-risk borrowers or those less likely to benefit from credit supply interventions.
  - Evidence of higher risk premiums for non-SOE borrowers and SOEs with weak government support following default outcome changes.

- Impacts on capital allocation and productivity
  - Older and predominantly state-owned firms that react most to credit supply interventions tend to have relatively weak productivity compared to private peers.
  - Interest rate easing is effective in boosting investment among financially constrained smaller firms with higher productivity and weakens investment among low productivity firms.
  - Credit policy easing likely generates a larger share of incremental new investment from relatively low productivity firms, implying weaker aggregate productivity growth in the short term and potentially over the longer term.
  - Cumulative impact on productivity growth depends on intensity and frequency of future tightening cycles and symmetry of cross-firm effects during tightening episodes.

- Financial inclusion (MSE-targeted) credit policies
  - Narrowly targeted interventions may create positive spillovers for marginal and constrained firms if well designed.
  - Bank risk aversion is more acute in the MSE segment, leading lenders to prefer collateralized borrowers (e.g., residential real estate) over entrepreneurs.
  - Monitoring difficulties increase risk of regulatory arbitrage.
  - Since introduction of MSE credit targets in 2018, privately owned and lower-rated firms have seen a steady net contraction in corporate bond issuance outstanding.
  - National Audit Office report (June 2022): 364 of 517 audited financial inclusion loans borrowers were not actually operational business entities, suggesting a high rate of arbitrage and routing of funds to housing market investments or ineligible large firms.
  - Evidence linking MSE company formation to house price appreciation at the city level highlights potential arbitrage-driven asset market spillovers.

### Policy recommendations
- Maintain interest rates as the primary monetary policy instrument
  - Interest rates have clear, broad-based demand effects on cyclically vulnerable business segments and do not rely on financial institutions’ risk preferences or balance sheet capacity.
  - Interest rate easing can operate rapidly and help limit scarring by reducing firm closures and employment losses.
  - Increased financial risk-taking from rate cuts should be addressed with prudential tools: regulation, supervision, and macroprudential policy.

- Use quantity-based credit instruments sparingly and market-based
  - Credit policies have a role when clear market failures exist or to amplify interest rate shocks, but should not replace interest rates as primary tool.
  - Prefer market-based incentives (for example, targeted re-lending facilities that provide interest cost subsidies for qualifying loans to MSEs).
  - Avoid administrative loan growth requirements, loan pricing mandates, or other non-market features that can conflict with prudential underwriting, raise arbitrage risks, and create financial vulnerabilities.

### Conclusion
- Interest rate policies: strong effects on activity among cyclical and financially constrained firms—should continue to play the primary role in managing cyclical fluctuations.
- Credit policies: limited broad-based credit channel effects; useful when market failures or amplification needs are clear, but should be market-based and complementary to interest rate policy to avoid arbitrage and adverse productivity consequences.

*Source: 1chnea2023002 - 23. In ownership-based groupings, credit policy shocks are also statistically significant (PDF chapter).*

### Appendix I. Additional Empirical Results

### Appendix I. Additional Empirical Results

### Shock definitions and estimation details
- Each time period t is one half-year.
- Interest rate shocks are unexpected 25 basis point decreases to the 1-year interest rate swap rate on days of monetary policy announcements.
- Credit policy shocks are unexpected 12.5 percentage point increases in credit above trend levels.
- Confidence intervals shown are 90 percent.
- Sources: Capital IQ; CEIC Data Company Limited; WIND; Bloomberg; and IMF staff calculations.

### Firm-level investment impulse responses by firm size (Interest Rate vs Credit Policy shocks)
- Table 1 — Robustness: Average Effect of Interest Rate Shock: By Firm Size & Shock Direction (∆Yi,t ... ∆Yi,t+4)
  - Small: Easing
    - ∆Yi,t = -0.09 (0.17)
    - ∆Yi,t+1 = -0.07 (0.27)
    - ∆Yi,t+2 = -0.41*** (0.14)
    - ∆Yi,t+3 = -0.35** (0.14)
    - ∆Yi,t+4 = 0.01 (0.11)
  - Small: Tightening
    - ∆Yi,t = 0.65** (0.3)
    - ∆Yi,t+1 = 0.6 (0.39)
    - ∆Yi,t+2 = 0.16 (0.28)
    - ∆Yi,t+3 = -0.52* (0.29)
    - ∆Yi,t+4 = -0.24 (0.23)
  - Mid-size: Easing
    - ∆Yi,t = -0.08 (0.09)
    - ∆Yi,t+1 = -0.15** (0.06)
    - ∆Yi,t+2 = -0.1* (0.05)
    - ∆Yi,t+3 = 0 (0.06)
    - ∆Yi,t+4 = -0.02 (0.08)
  - Mid-size: Tightening
    - ∆Yi,t = 0.16 (0.26)
    - ∆Yi,t+1 = 0.58** (0.24)
    - ∆Yi,t+2 = 0.27 (0.17)
    - ∆Yi,t+3 = -0.27 (0.34)
    - ∆Yi,t+4 = -0.24 (0.32)
  - Large: Easing
    - ∆Yi,t = -0.2** (0.09)
    - ∆Yi,t+1 = -0.11* (0.06)
    - ∆Yi,t+2 = -0.05 (0.05)
    - ∆Yi,t+3 = -0.04 (0.05)
    - ∆Yi,t+4 = 0.06 (0.06)
  - Large: Tightening
    - ∆Yi,t = 0.49** (0.19)
    - ∆Yi,t+1 = 0.17 (0.28)
    - ∆Yi,t+2 = -0.1 (0.09)
    - ∆Yi,t+3 = -0.41* (0.2)
    - ∆Yi,t+4 = -0.42* (0.22)
  - Controls: Y for all horizons.
  - Observations by horizon: 95,213; 83,461; 73,791; 63,740; 55,183.
  - Standard errors reported in parentheses. Significance: * p<.10, ** p<.05, *** p<.01.

- Table 2 — Robustness: Average Effect of Credit Policy Shock: By Firm Size & Shock Direction (∆Yi,t ... ∆Yi,t+4)
  - Small: Easing
    - ∆Yi,t = -0.15 (0.09)
    - ∆Yi,t+1 = -0.05 (0.14)
    - ∆Yi,t+2 = -0.25 (0.16)
    - ∆Yi,t+3 = -0.18* (0.1)
    - ∆Yi,t+4 = 0.41** (0.16)
  - Small: Tightening
    - ∆Yi,t = 0.51*** (0.15)
    - ∆Yi,t+1 = 0.08 (0.22)
    - ∆Yi,t+2 = 0.08 (0.21)
    - ∆Yi,t+3 = 0.24 (0.18)
    - ∆Yi,t+4 = -0.42 (0.29)
  - Mid-size: Easing
    - ∆Yi,t = 0.07 (0.06)
    - ∆Yi,t+1 = 0.08 (0.1)
    - ∆Yi,t+2 = -0.15 (0.13)
    - ∆Yi,t+3 = -0.14 (0.14)
    - ∆Yi,t+4 = 0.15 (0.15)
  - Mid-size: Tightening
    - ∆Yi,t = 0.02 (0.13)
    - ∆Yi,t+1 = -0.06 (0.08)
    - ∆Yi,t+2 = 0.06 (0.16)
    - ∆Yi,t+3 = 0.11 (0.12)
    - ∆Yi,t+4 = -0.07 (0.1)
  - Large: Easing
    - ∆Yi,t = -0.09** (0.04)
    - ∆Yi,t+1 = 0.06 (0.06)
    - ∆Yi,t+2 = -0.02 (0.1)
    - ∆Yi,t+3 = 0.01 (0.11)
    - ∆Yi,t+4 = 0.37*** (0.05)
  - Large: Tightening
    - ∆Yi,t = 0.28** (0.11)
    - ∆Yi,t+1 = -0.07 (0.06)
    - ∆Yi,t+2 = -0.21** (0.09)
    - ∆Yi,t+3 = -0.1 (0.16)
    - ∆Yi,t+4 = -0.34*** (0.1)
  - Controls: Y for all horizons.
  - Observations by horizon: 101,297; 89,010; 78,898; 68,385; 59,424.
  - Standard errors reported in parentheses. Significance: * p<.10, ** p<.05, *** p<.01.

### Firm-level investment impulse responses by firm age (Interest Rate vs Credit Policy shocks)
- Table 3 — Robustness: Average Effect of Interest Rate Shock: By Firm Age & Shock Direction (∆Yi,t ... ∆Yi,t+4)
  - Young: Easing
    - ∆Yi,t = -0.32** (0.11)
    - ∆Yi,t+1 = -0.23** (0.09)
    - ∆Yi,t+2 = -0.2*** (0.07)
    - ∆Yi,t+3 = -0.07 (0.07)
    - ∆Yi,t+4 = -0.04 (0.1)
  - Young: Tightening
    - ∆Yi,t = 0.54 (0.31)
    - ∆Yi,t+1 = 0.54 (0.33)
    - ∆Yi,t+2 = 0.25 (0.25)
    - ∆Yi,t+3 = -0.52 (0.35)
    - ∆Yi,t+4 = -0.33 (0.29)
  - Middle-Aged: Easing
    - ∆Yi,t = -0.08 (0.08)
    - ∆Yi,t+1 = -0.14* (0.07)
    - ∆Yi,t+2 = -0.11** (0.05)
    - ∆Yi,t+3 = -0.04 (0.06)
    - ∆Yi,t+4 = 0.02 (0.08)
  - Middle-Aged: Tightening
    - ∆Yi,t = 0.49* (0.26)
    - ∆Yi,t+1 = 0.45 (0.28)
    - ∆Yi,t+2 = -0.02 (0.16)
    - ∆Yi,t+3 = -0.4 (0.31)
    - ∆Yi,t+4 = -0.37 (0.27)
  - Old: Easing
    - ∆Yi,t = -0.09 (0.08)
    - ∆Yi,t+1 = -0.1* (0.06)
    - ∆Yi,t+2 = -0.09 (0.05)
    - ∆Yi,t+3 = -0.02 (0.06)
    - ∆Yi,t+4 = 0.03 (0.07)
  - Old: Tightening
    - ∆Yi,t = 0.21 (0.15)
    - ∆Yi,t+1 = 0.43 (0.31)
    - ∆Yi,t+2 = 0.24 (0.14)
    - ∆Yi,t+3 = -0.25 (0.23)
    - ∆Yi,t+4 = -0.2 (0.21)
  - Controls: Y for all horizons.
  - Observations by horizon: 95,213; 83,461; 73,791; 63,740; 55,183.
  - Standard errors reported in parentheses. Significance: * p<.10, ** p<.05, *** p<.01.

- Table 4 — Robustness: Average Effect of Credit Policy Shock: By Firm Age & Shock Direction (∆Yi,t ... ∆Yi,t+4)
  - Young: Easing
    - ∆Yi,t = -0.15** (0.06)
    - ∆Yi,t+1 = -0.02 (0.14)
    - ∆Yi,t+2 = -0.15 (0.14)
    - ∆Yi,t+3 = -0.01 (0.08)
    - ∆Yi,t+4 = 0.33* (0.16)
  - Young: Tightening
    - ∆Yi,t = 0.36*** (0.11)
    - ∆Yi,t+1 = 0.1 (0.18)
    - ∆Yi,t+2 = 0.06 (0.19)
    - ∆Yi,t+3 = 0.13 (0.13)
    - ∆Yi,t+4 = -0.32 (0.22)
  - Middle-Aged: Easing
    - ∆Yi,t = -0.01 (0.05)
    - ∆Yi,t+1 = 0.04 (0.09)
    - ∆Yi,t+2 = -0.14 (0.12)
    - ∆Yi,t+3 = -0.09 (0.13)
    - ∆Yi,t+4 = 0.32*** (0.11)
  - Middle-Aged: Tightening
    - ∆Yi,t = 0.2 (0.12)
    - ∆Yi,t+1 = -0.06 (0.1)
    - ∆Yi,t+2 = -0.05 (0.14)
    - ∆Yi,t+3 = 0.02 (0.14)
    - ∆Yi,t+4 = -0.23 (0.15)
  - Old: Easing
    - ∆Yi,t = 0.03 (0.05)
    - ∆Yi,t+1 = 0.09 (0.07)
    - ∆Yi,t+2 = -0.14 (0.1)
    - ∆Yi,t+3 = -0.13 (0.12)
    - ∆Yi,t+4 = 0.24*** (0.07)
  - Old: Tightening
    - ∆Yi,t = 0.12 (0.12)
    - ∆Yi,t+1 = -0.11 (0.08)
    - ∆Yi,t+2 = -0.05 (0.1)
    - ∆Yi,t+3 = 0.06 (0.14)
    - ∆Yi,t+4 = -0.18** (0.06)
  - Controls: Y for all horizons.
  - Observations by horizon: 95,213; 83,461; 73,791; 63,740; 55,183.
  - Standard errors reported in parentheses. Significance: * p<.10, ** p<.05, *** p<.01.

### Firm-level investment impulse responses by productivity groupings
- Figure 2 summarizes impulse responses by firm productivity groupings (Lowest, Mid-Range, Highest productivity) for both interest rate shocks and credit policy shocks.
- Key estimation notes repeated: each t is a half-year; interest rate shocks = unexpected 25 basis point decreases to the 1-year interest rate swap rate; credit policy shocks = unexpected 12.5 percentage point increases in credit above trend; 90 percent confidence intervals shown.
- Visual results reported in the figure indicate heterogeneity across productivity groups, with scales showing response ranges from -15 to 20 percentage points (Lowest, Mid-Range, Highest productivity panels).

### Coordinated vs Uncoordinated credit and interest rate shocks
- Figure 3 presents firm-level investment impulse responses during periods of coordinated credit and interest rate shocks versus uncoordinated shocks.
- Panels show interest rate shock: Uncoordinated and Coordinated; credit policy shock: Uncoordinated and Coordinated.
- Vertical scales in these panels range from -30 to 50 percentage points, indicating larger potential investment responses when shocks are coordinated.
- Estimation notes repeated: each t is a half-year; interest rate shocks = unexpected 25 basis point decreases; credit policy shocks = unexpected 12.5 percentage point increases; 90 percent confidence intervals.

### Overall empirical patterns (from figures and tables)
- Interest rate easing effects:
  - Significant negative contemporaneous and near-term investment responses for Small firms at t+2 and t+3 (Table 1: -0.41*** at t+2 and -0.35** at t+3).
  - Young firms show significant negative responses at t through t+2 (Table 3: -0.32**; -0.23**; -0.2***).
  - Large firms show significant negative immediate responses at t and t+1 (Table 1: -0.2** at t; -0.11* at t+1).
- Interest rate tightening effects:
  - Positive contemporaneous increases for Small and Large firms at t (Table 1: Small 0.65**, Large 0.49**), but subsequent horizons can show negative responses (e.g., Small t+3 = -0.52*; Large t+3 = -0.41*).
  - Young and middle-aged firms show positive point estimates contemporaneously but with limited statistical significance (Table 3).
- Credit policy easing effects:
  - Small firms: mixed/negative short-term effects with a significant positive rebound at t+4 (Table 2: t+4 = 0.41**).
  - Large firms: immediate negative effect at t (-0.09**) but a strong positive effect at t+4 (0.37***).
  - By age: Young, Middle-Aged, and Old firms display positive t+4 effects in several panels (Table 4: Young t+4 = 0.33*; Middle-Aged t+4 = 0.32***; Old t+4 = 0.24***).
- Credit policy tightening effects:
  - Small firms show a significant positive contemporaneous effect (0.51*** at t) but wide variation at later horizons (Table 2).
  - Large firms show positive contemporaneous and negative later responses (Table 2: t = 0.28**; t+2 = -0.21**; t+4 = -0.34***).
- Coordinated shocks produce larger-magnitude investment responses (Figure 3 scales extend to ±50 percentage points), suggesting that simultaneity of credit and interest rate policy moves amplifies firm-level investment dynamics.

*Source: Appendix I. Additional Empirical Results, 1chnea2023002.*

### 3.      As growth moderated and objectives

### 3.      As growth moderated and objectives

### A. Challenges of the traditional power system
- Low generation asset utilization, low energy efficiency, high pollutant emissions, and wastage (curtailment) in renewable energy generation.
- Incompatibility between increasing importance of environmental welfare and the command-and-control pricing framework that:
  - Ensured cost recovery for coal plant investments through inflexible planned operation hours contracts.
  - Made renewable energy artificially less economical due to lack of wholesale power market.
- Regulatory framework lacked incentives to:
  - Decrease total energy consumption.
  - Develop renewable energy.
  - Provide related products other than energy, such as ancillary services required to maintain grid stability and security (Supponen and others, 2021).
- Inadequate inter-provincial power trading platforms and incentives prevented efficient provincial purchasing and dispatching of power.
- Insufficient incentives for grid companies to construct new grid networks connecting large renewable-producing regions to populous coastal regions, resulting in curtailment.

### B. Market reforms and expected benefits
- Transition from planned fair dispatch to economic dispatch:
  - Enables resources to compete based on short-run marginal costs and reflects economic optimization.
  - Expected outcomes: lower operational costs, increased efficiency, reduced capacity underutilization, and reduced renewable energy curtailment.
  - Adoption of a national electricity market convergent across all provinces with flexible transactions and upgraded transmission connectivity will allow power plants to set generation hours to optimize profitability and reduce need to add new coal power capacity.
- Market-based electricity pricing:
  - Enhances efficiency of the national Emissions Trading Scheme (ETS) by enabling carbon pricing to be passed through to final consumers.
  - Reduces demand for energy and incentivizes shift away from fossil fuel investment toward renewable energy.
  - Phasing out price controls and generation quotas while guiding generators to participate in markets helps recover costs and alleviate burdens from fulfilling energy and carbon intensity targets.
  - Reforms will optimize system cost, enhance flexibility, and level playing field for renewable energy.
- Coordination and harmonization of markets:
  - Encourages private sector investment in energy storage.
  - Allows coal plants to reduce operation and stand by for backup generation when necessary.
  - Stimulates development of ancillary services markets to compensate reserve capacity and generate incremental revenue streams to energy storage and idle coal fleets.
- Transition costs and distributional issues:
  - Economic dispatch will likely trigger exit of inefficient coal generators.
  - Active management required to compensate most vulnerable households and workers in coal- and high-energy intensive industries.

### C. Brief history and progress of power market reforms
- 2015 reforms:
  - Established market-based mid- to long-term electricity forward markets.
  - Enabled wholesale energy prices via negotiation or auction between generators/suppliers and large consumers.
  - Broadened ancillary services markets and piloted spot markets; eight spot market pilots operating in China.
- Since June 2020:
  - More private players allowed in forward market, including distribution, wholesale and energy storage companies.
  - An estimated 45 percent of total energy consumption being traded on the mid- to long-term market as of end-2021 (Qin, 2021).
- Late 2021:
  - Coal-fired power prices allowed to rise or fall by up to 20 percent from benchmark price levels.
- 2022 announcement:
  - Envisions establishment of a national electricity market by 2025 to optimize resource allocation, increase interprovincial power trading, and better support renewables integration.
- Remaining limitations as of 2022:
  - Dispatch and pricing largely determined administratively through planned fair dispatch mechanism.
  - Dichotomy between central and local governments' interests; provincial autonomy has tended to enhance provincial protectionism and self-sufficiency (Guo and others, 2020).
  - Heavy reliance on coal operated/owned by local governments complicates shift to flexible market-based mechanisms.
  - Increase in renewables may cause price volatility and potential stranded assets for long-standing investments by local governments.

### D. Barriers to reform and transparency issues
- Administrative pricing and dispatch do not incentivize flexible production and storage needed for reliable renewable integration.
- On-grid price of wind and solar plus transmission tariff in some provinces can exceed locally generated coal power, affecting local economies.
- Legal/regulatory hurdles, limited transparency, and lack of inter-provincial coordination:
  - Many provincial markets begin trial operation prior to making designs accessible to a wide range of actors.
  - Grid, dispatch, and power pricing often operate without public information platforms, hindering market participation, market monitoring, and energy demand-side management (Hove and others, 2021).
  - Potential incompatibilities among different provincial rules pose challenges for future integration.

### E. Economic dispatch, regional trading, and quantified benefits
- Economic dispatch and higher levels of regional trading:
  - End guaranteed offtake for coal-fired power under fair dispatch that incentivized coal capacity approvals.
  - Market-based pricing allows greater passthrough of costs to end users, introducing price volatility that should incentivize greater efficiencies in power use.
  - Decrease system cost and emissions by increasing utilization of zero-marginal-cost renewables and more efficient thermal generators.
- Quantified model results and estimates:
  - IEA (2019) shows maintaining current fair dispatch leads to major inefficiencies and high renewable curtailment.
  - Switch to economic dispatch would bring operational cost savings of about 11 percent per year in 2035.
  - Power sector carbon emissions would fall by 15 percent under economic dispatch.
  - Timilsina, Pang and Yang (2021): optimal electricity prices under economic dispatch could be lower by about 5 percent compared to prices under existing dispatch.
  - IEA (2019) modelling: power system can integrate renewable energy at over 20 percent of total generation without any curtailment by improved operations and increased physical interconnections.
  - Operational cost savings and CO2 emission reductions from inflexible to flexible dispatch bring savings of USD 63 billion annually (IEA modeling).

### F. Importance of power market reform for China’s climate ambitions
- Power sector transformation is essential because largest share of China’s emissions is energy-related.
- Intensity Targets:
  - China set carbon emissions peak before 2030 and net carbon neutrality before 2060.
  - Relied heavily on mandatory energy consumption standards and energy intensity targets.
  - Intensity targets have clashed with growth ambitions and energy security, as shown by the 2021 power crunch.
  - Barriers to inter-provincial trading and lack of local coordination increase vulnerability to localized power shortages and weather-induced climate shocks (e.g., droughts and dependence on hydro).
  - Current investment incentives are not aligned to expand storage facilities and other ancillary services to bring more renewables onto the grid.
- ETS:
  - National ETS could facilitate incentives but currently lacks teeth without significant power market reforms.
  - Administratively set pricing limits extent to which ETS-implied carbon price is passed to downstream sectors and consumers.
  - Accelerating dispatch reform would enable ETS to take carbon costs into account through a merit order dispatch system, allowing lower-emitting technologies to operate more often.

### G. Box 1 — The impacts of intensity targets on climate and energy
- Context:
  - China uses medium-term policy frameworks (Five-Year Plans) with binding reduction targets for energy intensity and carbon intensity.
  - Key interim climate targets in 14th FYP (2021-2025): 13.5 percent reduction for energy intensity and 18 percent reduction of carbon intensity of GDP.
  - A cap on energy consumption growth set at 2 percent per year.
- Scenario analysis based on two GDP paths:
  - “Baseline” growth path (based on October 2022 WEO) and a “high growth” 5.5 percent annual scenario after 2025.
- Findings:
  - Baseline scenario: Chinese carbon emissions would peak around the year 2026, four years ahead of current ambitions, and lead to around 5 percent more carbon emissions compared to 2020 levels. Energy consumption growth was above the 2 percent cap in 2021 but below the cap in subsequent years.
  - High growth (5.5 percent after 2025): carbon emissions would peak in 2029, one year before 2030 target, with carbon emissions having grown by over 7 percent compared to 2020 levels. Energy consumption cap would be violated in 2026 and 2027.
- Implications:
  - Intensity targets are highly dependent on growth trajectory; strong rebound in 2021 made targets challenging for local governments.
  - Even if carbon intensity falls by target amounts, absolute carbon emissions could still increase, requiring more intensive decarbonization after 2030.
  - Absence of absolute caps on coal use could force sharp capacity reductions later; average lifespan of new coal plants around 50 years, creating risk of stranded or premature retirements.

*PEOPLE’S REPUBLIC OF CHINA — INTERNATIONAL MONETARY FUND.*

### Box 2. China: China’s “Power  Crunch”  of 2021

### Box 2. China: China’s “Power Crunch” of 2021

### Overview and immediate causes of the 2021 power crunch
- The power supply shortage occurred in the second half of 2021 and highlighted tradeoffs between energy security and climate ambitions.
- In early 2021, the government set a target for energy intensity to decline by around 3 percent during the year, broken down into provincial targets.
- The uneven nature of the recovery led to a significant jump in energy consumption in 2021, driven by heavy industries: steel, non-ferrous metals, chemicals, and building materials (cement and glass), to satisfy increased manufactured export demand and a domestic construction and infrastructure investment boom.
- Consequence at the provincial level:
  - In the first half of 2021, there were more than 12 provinces lacking on both counts of the targets.
  - Several local governments rationed power supplies to meet intensity goals.
- Supply-side mechanisms and disruptions:
  - Electricity prices paid to generators were regulated, while coal prices were set on the market.
  - When coal prices rose, coal power plants found it unprofitable to supply electricity, cut back coal purchases, and ran down coal inventories.
  - Coal mines did not ramp up output in time because price and demand signals were dampened.
  - Additional supply-side disruptions included: an anti-corruption campaign in Inner Mongolia, mining safety campaigns, heavy rains, and an intensive restructuring of the coal mining industry.
  - Surging coal mine output eventually closed the gap.

### Power price control reforms and market changes
- Immediate regulatory responses and reforms:
  - China had a narrow price band for generation prices; in October 2021 reforms were passed allowing the band to modestly expand up to 20 percent.
  - Prices for high energy-consuming enterprises and spot market trading were no longer subjected to the price control band range.
  - All coal power plants were guided to participate in the market, accelerating phase-out of generation quota.
  - All industrial and commercial consumers were required to purchase electricity from the market.
  - These changes helped coal generators recover increasing costs to some extent, though it is not clear whether renewable energy utilization increased as a result.
- Early 2022 developments:
  - In early 2022, China published its 14th Five-Year Plan for energy and issued other important power market regulations, including guidelines calling for a unified national energy market.

### Role of power market reform and the ETS (paragraphs 12–14)
- Interaction between power market reform and the national ETS:
  - Progress in power market reform can significantly enhance the effectiveness of China’s national ETS.
  - In the current setting, emissions reductions from the ETS are unlikely to be cost effective.
  - Transitioning from administratively determined dispatch to economic dispatch could:
    - Allow markets to reflect carbon prices in electricity generation costs and directly impact dispatch decisions.
    - Enable cost passthrough from generators to energy consumers and strengthen incentives for demand-side response.
  - Without economic dispatch, the ETS risks playing a limited role in reducing power sector emissions because coal power plants would not need to adjust operations in response to ETS allowance allocation signals.
- Model-based comparative findings:
  - Model simulations show that compared to command-and-control approaches (imposing energy intensity and total energy consumption targets), the ETS in combination with power sector reforms would achieve the same emissions reductions at less additional cost to the system.
  - Rationale:
    - Allowance trading with flexible electricity prices deploys the most affordable emissions abatement measures first.
    - Mandatory energy consumption targets force technologies to reduce energy consumption by similar scales regardless of relative cost.
    - With more stringent allowances and economic dispatch, the ETS would encourage high-efficiency units to run more, improving utilization rates; less-efficient units would either serve as back-up or be retired.
  - Additional ETS benefits:
    - Encourages deployment and scaling of carbon capture, utilization and storage (CCUS) in the power sector.
    - Supports ancillary service markets that further aid renewable integration.
  - Caveat:
    - If externality costs are not internalized and high-emitting sources remain cost-competitive, power market reform might optimize electricity production costs in a way not aligned with a low-carbon transition.

### Ancillary services and renewable integration
- Importance of ancillary services:
  - Ancillary services markets are critical for flexibility in systems with high shares of renewable energy.
  - China’s ancillary services are dominated by peak shaving, reserves, voltage regulation, and frequency regulation.
  - Peak shaving allocates power according to predictable demand patterns; reserves provide system back-up.
- Current limitations:
  - Most ancillary services markets are at early stages, may only allow participation of some generators, are designed and operated by local governments, and are not necessarily compatible across provinces.
  - Integration of renewables requires technology and infrastructure to manage intermittency and transform it into stable supply.
- Reforms and signals:
  - Making ancillary services markets more market-based and freer, in tandem with power market reforms and the ETS, would signal for investment in needed technologies, including retrofitting coal plants for greater flexibility.
  - Recent NEA reforms to expand ancillary services markets and to change cost allocation to include power end-users for the first time are promising first steps.

### Policy implications (selected measures and priorities)
- Electricity pricing:
  - Make electricity pricing more cost-reflective, convergent across provinces, and flexible to market conditions.
  - Expedite elimination of the administrated price range set for market transactions with coal power plants, including gradually abandoning the pricing band around electricity.
- Spot market expansion:
  - Expand spot market trading to help reduce generation capacity reserve, increase flexibility, and improve renewable integration.
  - Scale up regional pilot spot markets toward a national spot market that offers flexible transactions for short time periods countrywide.
  - Require coordination between local governments to harmonize provincial power markets and increase transparency and independence of energy exchanges from grid, generation, and retail companies.
- Inter-provincial trading and grid integration:
  - Strengthen inter-provincial power trading platforms and incentives to ensure provinces purchase and dispatch power efficiently.
  - Current market does not create adequate incentives for grid companies to build new networks connecting large renewable-producing regions to populous coastal regions.
  - Integrating provincial grids would allow provinces to use reserve capacity elsewhere, potentially reducing the need for additional coal power capacity for reliability.
  - Requires both physical investment in inter-provincial transmission capacity and reform to move dispatch operation and responsibility from provincial to regional and national levels to enable dispatch optimization across provinces.
- Ancillary services and capacity mechanisms:
  - Incentivize ancillary services markets and potentially a capacity market to fairly compensate reserve capacity and other services that support reliability.
  - These markets can create revenue streams for energy storage and coal fleets not in regular use, enabling private investment in storage and allowing coal plants to provide backup and ancillary services.
  - Together with effective carbon pricing like the ETS, these reforms will optimize system cost, enhance flexibility, level the playing field for renewable energy, and recalibrate the role of coal fleets from baseload suppliers to supportive facilities for peak load and reserves.
- Investment climate and private participation:
  - Build a conducive investment climate to attract private sector participation, critical for renewable energy and energy storage investment scale.
  - Enhance predictability of the policy framework to encourage private investment.
  - Expand the green electricity certificate (GEC) market and allow larger participation from renewable generators and voluntary purchasers to enhance cash flow to private investors.
- Renewable capacity valuation and demand-side measures:
  - Revisit approaches and regulations to align the capacity value of renewable energy with international standards.
  - Improve economic dispatch and utilization of hydro power and energy storage to meet peak demand following international best practices.
  - Further harness demand-side management, including demand response, to slow peak load growth and support integration of variable renewable energy.

*Prepared by Wenjie Chen; content drawn from Box 2, "China: China’s “Power Crunch” of 2021."*

### 5.      Existing evidence points to lower financing costs of green bonds, better investment

### 5.      Existing evidence points to lower financing costs of green bonds, better investment returns on green assets, and potential climate-related risk mispricing

### Key empirical findings on green finance and asset returns
- For the same bond issuers, at least 60 percent of issuers obtained lower financing costs for their green bonds compared with non-green bonds of comparable maturity (interest rate spreads of AAA-rated green and other bonds at issuance, relative to central government bond yields).
- Investing in green bonds and green equities tended to yield higher investment returns:
  - Positive cumulative excess returns since 2018 (excess returns of green bonds and green equities are computed relative to enterprise bonds and overall equities based on CSI 300).
  - Higher frequency of positive monthly excess returns.
- Evidence of climate-related risk mispricing:
  - The Global Financial Stability Report (April 2020) found a temperature pricing anomaly in China, Hong Kong SAR, and several other economies, implying equity investors in these markets have not paid adequate attention to climate change.
  - A mispricing of climate-related risks could undermine the efficiency of resource allocation and potentially heighten financial stability risks.
- Access to bond finance by carbon-intensive sectors has become more limited over time:
  - Bond issuances of carbon-intensive sectors have been more limited compared with other sectors, and their net bond issuances even turned negative in 2017 and 2021.

### Composition and uses of existing green loans and bond financing
- Banks’ green lending (2022Q3) is allocated as:
  - Infrastructure: 45 percent
  - Clean energy: 26 percent
  - Energy saving and environmental protection: 14 percent
  - Other green investment: 16 percent
- Main green borrowers: utilities and transportation sectors.
- Green bond issuance patterns:
  - Onshore market: funding largely tapped by firms in utilities, industrials, and transportation sectors.
  - Offshore market: significant issuance by the real estate sector.
- Sectoral emissions context (2019):
  - Electricity and heating sector: responsible for about half of total emissions.
  - Manufacturing, construction and industrial processes: 33 percent.
  - Transportation: 8 percent.
  - Overall, total energy generation (including for manufacturing) accounted for about 80 percent of carbon emissions.

### Policy initiatives and recent regulatory updates in China
- 2016: Joint guidelines issued and approved by the State Council to develop China’s green financial system to mobilize private capital for green investment.
- Since 2017: Pilot programs and local policy measures to support green transformation; policy approach formulated around “three functions” and “five pillars” to achieve carbon peaking and carbon neutrality:
  - “Three functions”: resource allocation, risk management, and market pricing.
  - “Five pillars”: (i) improving green finance standards, (ii) strengthening disclosure requirements, (iii) enhancing incentive and restraint mechanisms, (iv) developing product and market systems of green finance, and (v) expanding international cooperation on green finance.
- February 2012: CBRC guideline on issuance of green credit.
- Q3 2018: People’s Bank of China (PBC) incorporated green finance evaluation into its macroprudential policy assessment (MPA).
- November 2021: PBC launched the Carbon Emission Reduction Facility (CERF) to provide low-cost funding for lending to enterprises in clean energy, energy saving and environmental protection, and carbon emissions reduction technological development:
  - To be eligible for the CERF, banks are required to disclose climate-related information on a loan basis.
  - As of end-June 2022, re-lending under the CERF amounted to RMB 182.7 billion.
- November 2021: Additional re-lending scheme introduced to support clean and efficient use of coal with current quota at RMB 300 billion:
  - As of June 2022, such re-lending amounted to RMB 35.7 billion.
- April 2021: Updated Green Bond Endorsed Project Catalogue adopted the principle of “do no significant harm” and removed certain carbon-intensive projects related to fossil fuels such as clean coal technology.
- July 2022: China Green Bond Principles issued by the Green Bond Standards Committee stipulate that all proceeds must be used to finance green projects.
- July 2021: PBC issued the guideline on environmental information disclosures for financial institutions (applies to banks, insurers, asset managers, and other financial institutions) — aligned with TCFD-style recommendations; timeframe for mandatory disclosures has not been set.

### Gaps, risks, and areas for further reform
- Data and disclosure weaknesses:
  - National carbon trading system operation hampered by data issues.
  - Onshore intermediation of green finance does not generally rely on external review of climate-related information.
  - Existing disclosure regimes are limited; mandatory disclosures currently apply to only some nonfinancial firms in certain heavy-pollution industries and to bond issuers violating environmental laws.
  - For listed firms, guidelines on ESG reporting remain absent and existing reporting typically lacks quantitative information such as carbon emissions.
- Green taxonomy and alignment:
  - Prior non-alignment of onshore Chinese green bonds stemmed from lenient use-of-proceeds requirements (for example, exchange-traded bonds and enterprise bonds) and inclusion of carbon-intensive projects in the taxonomy.
  - Recent updates to the Green Bond Endorsed Project Catalogue and China Green Bond Principles move frameworks closer to international standards, but a unified economy-wide taxonomy could provide a stronger anchor.
- Bank incentives and accountability:
  - Banks are major green finance intermediaries driven by policy guidance; without market-oriented incentives and transparency, banks may not be intrinsically motivated to ensure funds finance genuine green investment.
  - Forcing banks to expand green lending via quantitative targets risks credit misallocation; a regulatory framework to ensure availability and accuracy of climate-related information and to define a transition-consistent taxonomy is preferable.
  - Policy support (e.g., subsidized funding, credit guarantees) could be conditioned on climate-related disclosures and meeting carbon emissions reduction targets, similar to sustainability-linked debt designs.

### Policy recommendations and priorities (summary)
- Strengthen the climate information architecture:
  - Develop high-quality climate-related data, proper definitions, and appropriate disclosures to enable market-oriented climate finance and limit greenwashing.
  - Implement a more comprehensive mandatory disclosure regime covering carbon emissions and exposures to physical and transition risks for firms, financial institutions, and listed firms.
  - Ensure data quality via robust internal controls and independent external review.
- Improve green and transition taxonomies:
  - Consider a unified, economy-wide taxonomy (building on the updated Green Bond Endorsed Project Catalogue) that applies across financial products and economic activities.
  - Develop a transition taxonomy to provide a clearer path for carbon-intensive activities to adjust or be phased out, consistent with carbon neutrality.
  - Adhere to “do no significant harm,” ensure material contributions to environmental goals, be guided by scientific evidence, and keep taxonomy dynamic.
- Strengthen disclosure requirements:
  - Prepare for adoption of emerging international disclosure standards (for example, ISSB) and enhance alignment with TCFD recommendations in the interim.
  - Apply mandatory disclosure requirements to all financial institutions and market participants (including listed firms and green bond issuers).
- Improve banks’ accountability and market-based climate financing:
  - Establish a regulatory framework to enhance governance and accountability of banks’ climate financing, limit greenwashing, and ensure appropriate climate-related risk management.
  - Use policy support (subsidized funding, credit guarantees) conditionally and link subsidized funding benefits to meeting carbon emissions reduction targets.
- Develop transition finance and effective carbon pricing to guide capital toward appropriate climate-related investments.

*Source: IMF staff summarization of chapter text.*

### 17.      More  vibrant market-based financing, along with a more diversified investor base,

### More  vibrant market-based financing, along with a more diversified investor base, could significantly strengthen China’s climate finance ecosystem.

### Market-based financing and green bond standards
- Market-based financing could help fill financing gaps when bank financing becomes constrained for entities/activities without credible pathways to carbon neutrality by mobilizing private capital from a more diversified investor base.
- Improving green and climate financing instruments and fostering green and sustainable investing are required, in addition to a strong climate information architecture.
- China has updated its green bond standards (2022 China Green Bond Principles); rigorous implementation would be key to limit greenwashing.
- Suggested enhancements to Chinese standards:
  - Make external review for pre-issuance assessment and post-issuance verification of proceeds management mandatory, similar to the CBI’s Climate Bonds Standard and the European Union’s proposed Green Bond Standard.
  - Introduce a harmonized framework for impact reporting to improve information comparability.
  - Require bond issuers to consider social and environmental risks associated with green projects to ensure appropriate risk management.
  - Require greater transparency about the use of proceeds for refinancing existing green projects.
- Implementation challenges and observations:
  - A notable portion of Chinese green bonds do not feature any external review.
  - Proper oversight of third-party evaluation and certification agencies is critical to ensure credibility of disclosures by green bond issuers.
  - In September 2021, the China Green Bond Standards Committee introduced operational rules for third-party evaluation and certification agencies; in September 2022, the first batch of 18 institutions were authorized to provide green bond assessment and certification services.
- Credit mispricing in onshore bond markets hinders development of green and sustainable bond markets:
  - Private nonfinancial firms’ bond issuances accounted for 5 percent of total green bond issuances by nonfinancial firms (onshore market share very limited).
  - Market perception of implicit state support to state-owned enterprises and local government financing vehicles should be phased out in an orderly manner.
  - Improve credit ratings transparency on issuers’ standalone financial strength and potential government support.

### Developing transition finance
- There is huge demand for transition finance as China moves toward a carbon-neutral economy; carbon-intensive sectors still need funding to undergo the transition (examples: retrofits of airline fleets; capture and utilization of gas; phasing-out of stranded assets).
- China has been developing financing instruments to support the transition (for example, sustainability-linked bonds and hybrid green bonds), and some Chinese entities have issued transition bonds overseas in accordance with international standards.
- Guidance for a transition finance framework:
  - A transition taxonomy aligned with China’s carbon peaking and carbon neutrality objectives is recommended; qualifying transition activities should have credible transition pathways consistent with science-based evidence and ambitious timeframes and must not lock in high carbon-emissions technology over extended periods.
  - The transition concept can cover carbon-intensive sectors and interim activities; the CBI’s framework considers five categories for transition finance, including near-zero activities, activities with existing pathways to zero, investments that support significant emissions reductions for activities without pathways to zero (example: international aviation), interim activities to be phased out (example: energy from municipal waste), and investments that help phase out stranded activities (example: electricity generation using coal). Enabling activities that provide essential goods and services to qualifying green and transition activities would also be eligible.
  - A transition finance framework should include taxonomy and underwriting standards and complement the existing green taxonomy; guiding principles for green finance standards (use of proceeds, project evaluation and selection process, management of proceeds, and reporting) should remain applicable to transition financing instruments.
  - Financial products could include debt financing instruments with use-of-proceeds requirements for eligible transition projects and financing costs contingent on environmental performance.

### Managing climate-related financial risks
- Careful monitoring and proactive management of climate-related risks can help safeguard financial stability.
- Major Chinese banks extended nearly 30 percent of their total loans to firms in utilities and energy, transportation and logistics, and manufacturing sectors, which tend to feature carbon-intensive activities.
- Transition risks could materialize from changes in climate policy, technological advancement, and market sentiment, resulting in stranded assets and losses to financial institutions.
- Recommendations to incorporate climate-related risks into prudential policy and market conduct frameworks:
  - Enhance supervisory capacity for climate-related risks, including supervisory processes, data collection, and analytical capability.
  - The CBIRC issued the Green Finance Guideline for Banking and Insurance Sectors in June 2022 to outline supervisory expectations; it complements the PBC’s guideline on environmental information disclosures. The CBIRC expects banks and insurers to put in place appropriate arrangements for governance, strategy, and risk management to manage climate-related risks within one year.
  - Set supervisory expectations for all financial institutions and monitor progress as part of regular supervisory processes; boards should be accountable for climate resilience and oversee climate strategy; climate considerations should be embedded in overall strategy and investment management; climate-related disclosures should be provided, including at the investment product level for investment funds.
  - Promote climate-focused stress testing and scenario analysis to assess climate impacts under different pathways; the PBC conducted a pilot climate stress testing exercise for 23 major banks in 2021 focusing on thermal power, steel, and cement sectors.
  - Ensure adequate capital buffers: traditional prudential frameworks may capture some climate-related risks, but Pillar 2 capital requirements could be employed to ensure sufficient buffers based on identified climate-related exposures.
  - Avoid using preferential risk weights in the absence of clear empirical evidence on the relationship between financial risks and greenness of exposures, as this would be incompatible with a risk-based regulatory approach.
- Role of the central bank (PBC):
  - The PBC could take climate change into account when carrying out policy functions and duties.
  - NGFS recommended central banks disclose governance, strategy, risk management, and the approach to climate-related risks and opportunities. Disclosures should include high-level approach, governance structure around monetary policy/asset management/financial stability/internal operations, strategy for identifying and assessing climate-related risks, measures regarding risks and opportunities, and risk management of climate-related exposures associated with investment portfolios and credit facilities.
  - The PBC could consider publishing a sustainability report to exemplify how financial institutions should approach climate-related disclosures.
- Embed climate change into systemic risk oversight:
  - Climate change could amplify macro-financial vulnerabilities such as elevated indebtedness, credit mispricing, and weaknesses of some small banks; a macroprudential approach is warranted to manage climate-related risks over the long term.
  - Strengthen systemic risk oversight by accounting for climate change considerations, enhancing analytical capacity, closing data gaps to assess climate-related exposures, and ensuring sufficient capital buffers.

### Conclusion — key actions and policy recommendations
- China is leading in developing green finance but further actions are needed to strengthen the financial system’s capacity to finance the climate transition.
- To secure a vibrant climate finance ecosystem, consider actions including:
  - Strengthening the foundation for well-functioning, market-oriented climate finance by improving the quality and comparability of data, setting proper green and transition definitions, and ensuring appropriate climate-related disclosures.
  - Improving banks’ accountability for climate investing, supported by enhanced climate-related lending practices and market-oriented mechanisms.
  - Deepening market-based climate financing and developing a more diversified investor base to complement the existing bank-dominant green finance intermediation.
  - Enhancing standards of green and climate financing instruments, with a focus on improving reliability of information via credible external reviews to ensure transparency, limit greenwashing, and avoid locking in stranded assets.
  - Developing a transition finance framework aligned with the overall climate objective to finance a credible climate transition.
- To enhance financial system resilience against climate change, key actions include:
  - Incorporating climate-related risks into prudential policy and market conduct frameworks by setting supervisory expectations and requirements on developing a sustainable business in response to climate change and monitoring progress.
  - Mandating appropriate disclosure requirements to ensure transparency of financial institutions’ actions and improve accountability.
  - Strengthening systemic risk oversight by accounting for climate change considerations, enhancing analytical capacity, closing data gaps, and ensuring sufficient capital buffers to absorb potential losses induced by climate change.

*Source: PEOPLE’S REPUBLIC OF CHINA — Chapter 17 (provided content).*

### References

### References

### Cited policy documents, reports, and technical papers
- Chen, Yulu. 2021. “Green Finance’s ‘Three Functions’  and ‘Five Pillars’ for Realizing “30·60 Goals.” Available at the People’s Bank of China’s website.
- China Banking and Insurance Regulatory Commission. 2022. “Green Finance Guidelines for Banking and Insurance Sectors.” Chinese version available at the State Council’s website.
- Chinese authorities. 2016. “Guidelines for Establishing the Green Financial System.” Available at the People’s Bank of China’s website.
- Chinese authorities. 2021. “Green Bond Endorsed Projects Catalogue”, Available at the People’s Bank of China’s website.
- Climate Bonds Initiative. 2020. “Financing Credible Transitions: How to Ensure Transition Label Has Impact.” Climate Bonds White Paper. Available at Climate Bonds Initiative’s website.
- Climate Bonds Initiative. 2022. “Green Bond China Investor Survey 2022.” Available at Climate Bonds Initiative’s website.
- Climate Bonds Initiative, and CECEP Hundred Technical Service (Beijing) Co., Ltd. 2022. “Transition Finance in China: Latest Development and Future Outlook.” Available at Climate Bonds Initiative’s website.
- Climate Bonds Initiative, and China Central Depository & Clearing Research Centre. 2022. “China Green Bond Market Report 2021”. Available at Climate Bonds Initiative’s website.
- Climate Bonds Initiative, and SynTao Green Finance. 2022. “China Green Finance Policy: Analysis Report 2021.” Available at Climate Bonds Initiative’s website.
- European Commission. 2021. “FAQ: What Is the EU Taxonomy and How Will It Work in Practice?”
- Green Bond Standards Committee. 2022. “China Green Bond Principles.” Available at National Association of Financial Market Institutional Investors’ website.
- G20 Sustainable Finance Working Group. 2021. “G20 Sustainable Finance Roadmap”. Available at G20 Sustainable Finance Working Group’s website.
- G20 Sustainable Finance Working Group. 2022. “G20 Sustainable Finance Report”. Available at G20 Sustainable Finance Working Group’s website.
- International Capital Market Association. 2020. “Climate Transition Finance Handbook: Guidance for Issuers.” Available on International Capital Market Association’s website.
- International Monetary Fund. 2022. “Scaling up Private Financing in Emerging Market and Developing Economies: Challenges and Opportunities”, Global Financial Stability Report. Washington.
- Liu, Guiping. 2021. “On Leveraging the Financial Market to Achieve the Goal of Carbon Peaking and Carbon Neutrality.” Speech at the 13th Lujiazui Forum. Available at the People’s Bank of China’s website.
- Ministry of Ecology and Environment. 2021. “The Plan for the Reform of the Legal Disclosure System of Environmental Information.” Chinese version available at Ministry of Ecology and Environment’s website.
- Monetary Authority of Singapore. 2022. Sustainability Report 2021/2022. Singapore.
- Network for Greening the Financial System. 2021. “Progress Report on the Guide for Supervisors.” Technical Document, October.
- Network for Greening the Financial System. 2021. “Guide on Climate-related Disclosures for Central Banks.” Technical Document, December.
- Network for Greening the Financial System. 2022. “Enhancing Market Transparency in Green and Transition Finance.” Technical Document, April.
- Network for Greening the Financial System. 2022. “Capturing Risk Differentials from Climate-related Risks: A Progress Report.” Technical Document, May.
- People’s Bank of China. 2021. “Guidelines for Financial Institutions on Environmental Information Disclosures.” Chinese version available at the Financial Industry Standard of the People's Republic of China’s website.
- State-owned Assets Supervision and Administration Commission. 2022. “Work Plan for Improving the Quality of Listed Companies Held by Central Enterprises.” Chinese version available at the State Council’s website.
- Task Force on Climate-related Financial Disclosures. 2017. “Recommendations of the Task Force on Climate-related Financial Disclosures”.

### Key findings, projections, and diagnostics on China’s growth prospects (extracted from chapter text)
- Structural context and headwinds:
  - China’s potential growth has slowed and is facing several headwinds projected to further lower potential growth in the medium- to long-term.
  - Unique additional pressure: diminishing returns of investment-led growth, with excessive investment channeled towards relatively less productive SOEs and activities such as real estate.
- Drivers of high savings and demand imbalances:
  - Chinese households have an exceptionally high savings rate, reflected in a low consumption share in GDP.
  - High household savings driven by precautionary savings due to gaps in the social protection system and falling job security, and China’s aging population.
  - During the pandemic, zero-COVID policies increased household savings amid high uncertainty, weaker labor markets, and subdued private consumption.
- Investment composition and vulnerabilities:
  - Real estate sector accounted for around 20 percent of China’s GDP.
  - Augmented government debt-to-GDP ratio reached more than 100 percent in recent years.
  - Post-GFC, growth became increasingly dependent on investment in infrastructure and housing.
  - Nonfinancial private sector credit-to-GDP ratio increased by 45 percentage points during 2012-2016.
- Productivity and returns to capital:
  - China’s marginal product of capital has been falling.
  - The marginal product of capital series is shown for 2000–2019 (smoothed using HP filter).
  - Productivity growth in manufacturing slowed considerably following the global financial crisis, linked to declining business dynamism and a significant presence of less-productive SOEs.
  - Large productivity gaps between SOEs and private firms persist.
- Methodology and historical decomposition:
  - Potential growth estimated using a standard Cobb-Douglas production function: Y_t = A_t K_t^α (L_t h_t)^{1−α}.
  - Conventional coefficients used: α = 0.4 and 1−α = 0.6.
  - Sectoral decomposition incorporated: primary, secondary, tertiary sectors; real estate incorporated in the secondary sector.
  - Sustainable GDP growth for 2012-18 estimated at 5.3 percent rather than actual GDP growth of 7.2 percent (after adjustments for excessive credit expansion per Chen and Kang (2018)).
  - Historical aggregate TFP growth fell from 3.7 percent in the 2000s to 1.9 percent from 2010-19.
  - Within-sector TFP growth fell from averages of around 3-4 percent in the 2000s to 1 percent or less in the 2010s.
- Estimated potential growth levels:
  - China’s potential growth fell from a peak of around 10 percent to less than 5 percent.
  - For 2021, estimated potential growth is 4.7 percent, with weaker TFP growth explaining the largest part of the drop from its peak.
- Forecast approach:
  - Forecasts provide a baseline and an upside scenario; the upside scenario illustrates one possible path under a set of simultaneous reforms.
  - A bottom-up approach is used to forecast each factor in the production function and to derive scenario projections.

*Italic: Source — 1chnea2023002 - References (extracted content).*

### 17.      In our baseline scenario, we assume no significant structural reforms, but a return to

### 1chnea2023002 - 17.      In our baseline scenario, we assume no significant structural reforms, but a return to

### Baseline scenario: key assumptions
- Labor:
  - Evolves in line with the UN’s medium fertility growth scenario.
  - Implicitly assumes the average retirement age of 54 will remain constant.
  - In the absence of significant rebalancing, sectoral labor shares converge to advanced economy shares only by 2050.
- Human capital:
  - Continues growing at its current rate, i.e., assumes no lasting scarring from ZCS.
- Investment / capital:
  - In the absence of significant reforms towards rebalancing, investment is assumed to remain a large share of GDP, even as it grows less than before.
  - Investment-to-GDP ratio will fall by about 1 percentage point in the long term from its current level.
  - Slow factor reallocation implies capital stock shares converge to current advanced economy shares only by 2050.
- TFP:
  - Within-sector TFP growth is assumed to remain constant at its current level.
  - Sectoral reallocation continues in line with labor and capital share assumptions, with the reallocation share in total TFP gradually falling over time.

### Baseline scenario: projections and comparisons
- Potential growth projections:
  - Potential GDP growth rates could drop to about 4 percent on average between 2023-27 and 3 percent on average over 2028-37.
  - Implies per capita growth rates of similar magnitude over the same horizon.
- Historical comparators (last 10 years averages):
  - 6 percent sustainable GDP growth.
  - 7 percent actual GDP growth.
  - 6 percent real per capita GDP growth.

### Upside scenario: reform assumptions (phased linearly over 15 years starting in 2023)
- Investment / capital:
  - Investment-to-GDP ratio falls to advanced economy (AE) average of 22 percent over 15 years (upside table).
  - Under the upside demand-side rebalancing assumption, the investment-to-GDP share is assumed to fall by around 18 percentage points over the reform horizon as it converges to current advanced economy ratios.
- Capital and labor reallocation:
  - Convergence to AE shares within 15 years (capital shares).
  - Reallocation towards services and AE shares over reform horizon of 15 years (labor shares).
- Retirement age reform:
  - Retirement age gradually moves by 10 years (from 55 to 65) for females and by 5 years (from 60 to 65) for males over long-run.
  - Goal: enlarge the potential workforce.
- Human capital / education:
  - Human capital converges to current AE level within 15 years under education reform assumptions.
- SOE and market-dynamism reforms:
  - SOE reforms close an estimated SOE-POE productivity gap in the secondary sector of around 6 percent by year 2038 (assumed extended to the entire secondary sector).
  - Market-dynamism reforms boost productivity in the secondary sector by 1 percentage point over the reform horizon.
- Tertiary sector TFP and reallocation:
  - Reallocation of resources to tertiary sector assumed to boost tertiary sector TFP by 0.05 percentage points per additional percentage point of higher labor share.

### Upside scenario: projected impacts and gains
- Growth:
  - Average GDP growth rates of about 4.5 percent between 2023-37 under the upside scenario.
  - A similar per capita growth rate over 2023-37.
- Level effects:
  - Reforms are estimated to lift the level of real GDP by around 2.5 percent by 2027 compared to the baseline scenario.
  - Reforms are estimated to lift the level of real GDP by around 18 percent by 2037 compared to the baseline scenario.
- Consumption and demand rebalancing:
  - Higher consumption share of GDP by around 18 percentage points in 2037 under the upside scenario.
  - The higher consumption share translates to an improvement in consumption of 75 percent over the same time period.
- Environment and emissions:
  - Direct effect on CO2 emissions: a reduction of about 15 percent by 2037 (Chateau and others, 2022).
- Public finances and risk:
  - Augmented public debt by 2037 falls from 173 percent of GDP under the baseline to 146 percent of GDP in the upside scenario.
  - Corporate debt burden would also fall, mainly because of higher growth.
  - Reduction in saving rates would make the economy less prone to asset bubbles and provide a sustainable driver for non-real estate investment.
- Distribution and climate co-benefits:
  - Reforms would ensure growth benefits are shared more broadly and offer faster progress towards China’s climate goals.
  - Productivity-enhancing SOE reforms could support decarbonization, noting estimates that SOEs generate about half of the country’s total GHG emission.

### Policy implications and recommendations
- Need for comprehensive reforms to address:
  - Aging population and slowing aggregate productivity.
  - High investment rates that have pushed investment into less productive sectors.
- Rebalancing:
  - Shift away from an investment-led, carbon-intensive growth model toward more consumption-driven growth to expand services and rebalance away from excessive, low-productivity investment.
  - A budget-neutral recomposition of fiscal expenditures toward households, including strengthening the social protection system, to reduce the household savings rate and support consumption.
- Structural reforms:
  - Market-based structural reforms to address productivity issues, reduce resource misallocation, and reallocate capital from SOEs and low-productivity investment (infrastructure and real estate) into more productive manufacturing or services sectors.
  - SOE reforms to enhance productivity in the use of carbon-intensive inputs and stimulate innovation in renewables.
- Labor supply:
  - Gradual retirement age increases to enlarge the potential workforce.
- Education:
  - Improve access to and quality of education to boost human capital toward AE levels.

### Conclusion: risks and long-term outlook
- Without reforms, aging and declining productivity would likely continue to suppress growth over the long term, beyond the forecast horizon.
- Additional downside risks include:
  - Prolonged adherence to zero-COVID policies.
  - Geoeconomic fragmentation and reduced technology knowledge exchange amid technological decoupling.
- Comprehensive reform package consequences:
  - Higher sustainable growth, reduced macroeconomic risks, higher welfare, and faster progress toward climate goals.

### Methodology (brief)
- Framework:
  - Standard Cobb-Douglas production function: Yt = At Kt^α (Lt ht)^(1−α).
  - Log-linearized growth-rate decomposition (TFP as residual).
  - Sectoral decomposition into primary, secondary, tertiary sectors with within-sector TFP growth and reallocation effects.
- Key equations and components preserved from the chapter: production function, growth-rate decomposition, historical decomposition, sectoral decomposition and reallocation terms.

### Data sources (brief)
- Real GDP: NBS; sectoral GDP shares based on nominal GDP shares.
- Capital stock: Herd (2020), perpetual inventory method, real gross fixed capital formation (staff estimates based on NBS); sectoral capital stock initial period based on Wu (2016).
- Labor: proxied by working age population (15-59 for males, 15-54 for females); sectoral labor based on employment shares from NBS.
- Human capital: index from Penn World Tables 10.0, based on average years of schooling and returns to education.

*Source: IMF staff estimates.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2023/english/1chnea2023002.pdf_