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### DEFLATION VULNERABILITY AND INFLATION-AT-RISK
- Context and observations:
  - Monthly core inflation since the pandemic: ranged between 0.4 and 1.3 percent (fatter left tail) versus pre-pandemic range of 1 to 2.5 percent.
  - Nominal GDP growth has been below real GDP growth for 4 quarters starting in 2023Q2.
- Deflation Vulnerability Index (DVI) — construction and data:
  - Five indicator groups: commodity prices (pork added), output gaps, financial asset prices (stock returns; secondary market house price across 70 cities), exchange rates, and inflation expectations (household survey).
  - Money supply indicator excluded.
  - Method: PCA within each group → two principal components → deflation vulnerability factors (DVF); second-stage PCA on five DVFs → single DVI.
  - Historical note: multiple episodes with DVI below zero since 2003; the episode beginning in 2020 is the longest.
- Inflation-at-Risk (IaR) methodology:
  - Local projection quantile regressions: π_{t+h,τ} = β_{h,τ} DVI_t + γ_{h,τ} X_{t-1} + ε_t with τ emphasis at 10 (10th percentile).
  - Controls: past 12-month average core inflation and year-on-year growth in M2.
- Key IaR findings and magnitudes:
  - A one-unit increase in DVI → around 1.1 percentage point decline in core inflation at the 10th percentile of the 9-month ahead distribution.
  - A one-standard-deviation negative shock to output gaps → reduces 10th percentile of 9-month ahead core inflation by around 0.6 percentage points.
  - A one-standard-deviation negative shock to inflation expectations → reduces 10th percentile of 9-month ahead core inflation by around 0.4 percentage points.
  - Negative shocks to inflation expectations predict downside risk at the 10th percentile but are irrelevant at the median and 90th percentile.
- Probabilities of core deflation (9-month ahead):
  - Baseline projection: around 7 percent.
  - With 1-standard-deviation shock to DVI: 27 percent.
  - With 2-standard-deviations shock to DVI: 54 percent.
- Policy implications:
  - Stabilize property market, complete unfinished pre-sold housing, restructure distressed developers.
  - Target fiscal support to lower-income households (highest marginal propensity to consume).
  - Avoid excessive supply-side capacity expansion amid weak demand.
  - People’s Bank of China: further ease monetary policy stance, monitor and anchor inflation expectations, close negative output gap.
  - Strengthen monitoring using inflation-at-risk analysis and improve measurement of inflation expectations; strengthen central bank communication.

### SERVICE SECTOR PRODUCTIVITY, ALLOCATIVE EFFICIENCY AND INNOVATION
- High-level conclusions:
  - Structural transformation contributed positively to economy-wide TFP growth and innovation in the last 20 years.
  - TFP growth of market services exceeded that of the secondary sector.
  - Substantial and growing misallocation of capital and labor in the service sector, masked by high innovation.
- Stylized facts:
  - Services nominal value added rose from under 30 percent to over 50 percent of China’s nominal value added over four decades.
  - Market services generally positive TFP growth and levels, except real estate services.
  - Non-market services (healthcare, education) tend to have low or negative TFP growth and levels.
- Contributions to aggregate productivity:
  - Structural change and within-sector productivity both contributed positively; shift to market services was particularly strong in the 2000s.
  - Expansion of real estate services contributed negatively to overall TFP growth of market services.
- Allocative efficiency and innovation mechanisms:
  - Allocative efficiency measured via dispersion in revenue productivity of capital and labor across firms.
  - Data: Orbis on ~40,000 firms for allocative inefficiency calculations.
  - Market services show growing misallocation since 2003 despite rising innovation (innovation surge around 2015, e.g., mobile payments).
  - Possible frictions: preferential credit/subsidies to SOEs, regulatory barriers to entry, limits on nationwide operation, hukou constraints.
- Empirical metrics highlighted:
  - Negative growth in allocative efficiency in market services since 2003.
  - Rising innovation in market services offset the negative allocative efficiency contribution in aggregate TFP growth.
- Policy implications:
  - Further opening to trade.
  - Greater labor mobility (hukou reform).
  - SOE reforms to reduce preferential credit/subsidies and level the playing field.
  - Reduce regulatory barriers to market entry and nationwide operation.

### ALLOCATIVE EFFICIENCY — SECTORAL PATTERNS AND GAPS
- Cross-sectoral and quantitative indicators:
  - Allocative efficiency gap measured relative to weighted average of five major AEs (DEU, FRA, JPN, ITA, USA).
  - Gap smallest in construction and mining; largest in market services (hospitality, retail, information services).
  - Share of SOEs in sectoral value added, 2017:
    - Service sector: 34 percent
    - Industry: 21 percent
  - Average SOE’s revenue productivity in manufacturing and services (2018): about 3 percent below POEs.
  - China appears less open in services; higher restrictions on foreign entry, less regulatory transparency, more barriers to competition.
- Drivers of low allocative efficiency:
  - High trade restrictions in services (OECD STRI and subcomponents).
  - Large SOE presence in several service sectors (finance, transport, storage, post and communications).
- Policy recommendations to lift allocative efficiency:
  - Reduce restrictions to trade in services and interprovincial barriers.
  - Pursue SOE reform to reallocate capital and labor to more productive POEs.
  - Facilitate greater firm entry and exit across a unified national market.
  - Scale back industrial policies favoring specific companies/sectors.
  - Continue reduction of FDI and shareholding restrictions and expand pilot/national free trade zone liberalization.

### CHINA’S INTENSITY-BASED ETS (IB-ETS) AND REFORM SCENARIOS
- IB-ETS overview:
  - Allowances allocated annually: benchmark for technology class × production.
  - Aggregate production rise → total permits increase (in contrast to QB-ETS).
  - National IB-ETS introduced in 2021; planned extension to industry and introduction of auctioning (details/timeline not confirmed).
  - 2025 targets cited:
    - non-fossil electricity generation to reach 39%
    - renewable electricity consumption to account for 33% of total electricity consumption
    - 30 GW new energy storage capacity by 2025
    - 120 GW total pumped hydro storage by 2030
- Benchmarks (tons CO2 per MWh) — selected technology rows and values:
  - Conventional coal-fired units >400MW: 2022: 0.8177; 2025: 0.6549; 2030: 0.5420; 2035: 0.6476; % Reduction by 2030: -33.7%
  - Conventional coal-fired units ≤400MW: 2022: 0.8729; 2025: 0.6991; 2030: 0.5785; 2035: 0.6914; % Reduction by 2030: -33.7%
  - Unconventional coal-fired units (CFB): 2022: 0.9303; 2025: 0.7451; 2030: 0.6166; 2035: 0.7368; % Reduction by 2030: -33.7%
  - Gas-fired units: 2022: 0.3901; 2025: 0.3124; 2030: 0.2586; 2035: 0.3090; % Reduction by 2030: -33.7%
- Reform scenarios (NDC-consistent assumptions):
  - NDC path: peak carbon before 2030; cut carbon intensity of GDP by 65 percent from 2005 levels by 2030.
  - NDC-Consistent power sector emissions: 4.9 billion metric tons by 2030 (a reduction of 15 percent).
  - Scenarios: Baseline IB-ETS; Scenario 1 — Partial auctioning (1 to 25 percent by 2030); Scenario 2 — QB-ETS; Scenario 3 — Expand IB-ETS coverage to industry (Phase 1: cement, aluminum, iron and steel; Phase 2: add pulp and paper, other non-metal products, other non-ferrous metals, chemicals, petroleum refining).
- Modeling approach (CPAT + power plants module) and assumptions:
  - Models fossil fuel plants at province-technology class level; profit-maximizing q and heat rate decisions; administrative pricing arrangements included; firms shut down if unprofitable for three consecutive years; coal price endogenous; partial pass-through of costs to electricity prices.
  - Renewable generation share in 2030 rises from 37 percent to 44 percent when IB-ETS is considered.
- Key 2030 results (Text Table 2; year 2030 figures):
  - Change in Electricity Price from 2019:
    - No Policy: 28%
    - Baseline: Intensity-based ETS: 38%
    - Auction Permits: 41%
    - Quantity-based ETS: 45%
    - Include Industry, Phase 1: 35%
    - Include Industry, Phase 2: 34%
  - Government Revenues, % of GDP:
    - No Policy: 0%
    - Baseline IB-ETS: 0.23%
    - Auction Permits: 0.33%
    - QB-ETS: 0.25%
    - Include Industry, Phase 1: 0.20%
    - Include Industry, Phase 2: 0.20%
  - Welfare Gain, Relative to No Policy, % of GDP:
    - No Policy: 0.0%
    - Baseline IB-ETS: 0.43%
    - Auction Permits: 0.44%
    - QB-ETS: 0.49%
    - Include Industry, Phase 1: 0.56%
    - Include Industry, Phase 2: 0.56%
  - Power Sector Emissions Reduction (2030):
    - No Policy: 0%
    - Baseline IB-ETS: -15%
    - Auction Permits: -15%
    - QB-ETS: -15%
    - Include Industry, Phase 1: -10%
    - Include Industry, Phase 2: -8%
  - Production from Fossil Fuel Power Plants (millions of GJ):
    - No Policy: 19,648
    - Baseline IB-ETS: 17,271
    - Auction Permits: 17,268
    - QB-ETS: 17,173
    - Include Industry, Phase 1: 18,274
    - Include Industry, Phase 2: 18,586
  - Firm Profits, Relative to No Policy:
    - No Policy: 0.0%
    - Baseline IB-ETS: -26.3%
    - Auction Permits: -34.1%
    - QB-ETS: -18.2%
    - Include Industry, Phase 1: -6.0%
    - Include Industry, Phase 2: -3.7%
  - Emissions Intensity (mtons CO2/GJ):
    - No Policy: 0.291
    - Baseline IB-ETS: 0.283
    - Auction Permits: 0.283
    - QB-ETS: 0.284
    - Include Industry, Phase 1: 0.282
    - Include Industry, Phase 2: 0.282
  - Needed Change in Benchmark:
    - No Policy: N/A
    - Baseline IB-ETS: -35%
    - Auction Permits: -35%
    - QB-ETS: N/A
    - Include Industry, Phase 1: -12%
    - Include Industry, Phase 2: -8%
- Interpretation and trade-offs:
  - Extending IB-ETS to industry yields largest welfare gains; QB-ETS reduces fossil fuel electricity production most but raises electricity prices the most.
  - Auctioning increases electricity prices more than baseline IB-ETS but generates fiscal revenues (estimated around $40 billion by 2030, or 0.2 percent of GDP) usable for transfers or competitiveness mitigation.
  - Revenues plus transfers (e.g., 10 percent of new revenues as lump-sum to lowest 3 deciles) can reverse regressivity.
  - All reform scenarios dominate baseline IB-ETS in combined consumer/producer surplus plus government revenues over 2023–2035 aggregation.
- Recommendations:
  - Accelerate extension of IB-ETS to industrial firms (move coverage from 44 percent toward almost 80 percent of total CO2 emissions).
  - Use ETS revenues via partial auctioning for transfers to low-income households.
  - Gradually transition to a QB-ETS with an economy-wide emissions cap.
  - Reaccelerate power sector reforms to improve price flexibility and responsiveness.
  - Early mitigation action before 2035 to lower costs of reaching 2060 carbon neutrality.

### CORPORATE SECTOR VULNERABILITIES (NONFINANCIAL FIRMS AND PROPERTY DEVELOPERS)
- Aggregate corporate debt and structure:
  - Combined debt of nonfinancial firms and LGFVs: 172 percent of GDP as of end-2024Q1 (BIS).
  - Combined debt at end-2009Q1: 106 percent of GDP.
  - Excluding LGFV debt, corporate debt: 131 percent of GDP.
  - Financing sources: bank lending 74 percent, bond financing 15 percent, nonbank financing 9 percent.
  - Within bank lending: manufacturing, business services, and transport each ≈ 15 percent of bank lending.
- Leverage, profitability, liquidity (non-real-estate firms):
  - Total debt to total assets ratio ~38 percent (fluctuating); total liabilities-based ratio 66 percent in 2023.
  - Return on assets: 2.4 percent in 2017 → 1.9 percent in 2023.
  - Profit margins: 18.4 percent in 2017 → 16.3 percent in 2023.
  - Aggregate cash held by firms: 10 percent of total assets in 2023.
  - Short-term debt: 16 percent of total assets; net accounts payable: 2 percent of total assets.
  - Adjusted cash position (cash net of short-term debt and net accounts payable): negative 8 percent of total assets.
  - Expected cash position (including earnings of 5 percent of total assets and net interest expense of 1 percent of total assets): negative 4 percent of total assets.
  - Share of firms with negative common equity close to zero.
  - Slightly more than half of firms by assets reduced leverage recently; firms accounting for 13 percent of corporate sector assets reduced debt in 2020-23.
  - Central SOEs have highest leverage; private firms reduced debt more than SOEs.
- Vulnerability to shocks:
  - Permanent reduction in earnings by 10 percent → raises share of risky debt by 6 percentage points.
  - Permanent increase in funding costs by 1 percentage point → raises share of risky debt by 26 percentage points.
  - Temporary one-year shocks increase risky debt slightly; permanent shocks (more than three years) could structurally weaken debt-servicing capacity.
- Small firms:
  - Small firms show lower leverage, higher profitability, stronger liquidity in aggregate but thick tails with many loss-making firms.
  - Share of small firms with negative profits: 24 percent in 2023 (vs. 7 percent for all firms).
  - Share of small firms with structurally weak debt-servicing capacity: 9 percent (vs. 3 percent for all firms).
  - Rapid increase in bank lending to micro and small enterprises may reflect weakening underwriting.
- Policy priorities for corporate sector:
  - Maintain prudential norms; remove regulatory forbearance and require proper loss recognition and provisioning.
  - Facilitate debt restructuring and orderly exits; modernize corporate bankruptcy framework; special insolvency process for small businesses; nationwide personal insolvency framework.
  - Support market-based credit allocation; phase out policy guidance that creates preferential credit and implicit guarantees.
  - Develop contingency planning and market-wide liquidity support schemes for core funding markets (corporate bond, repo).
- Property developers — key metrics and risks:
  - Total liabilities to total assets for property developers: 80 percent in 2023.
  - Debt accounted for 39 percent of total liabilities for property developers.
  - Unearned revenues (presales): 26 percent of total liabilities.
  - Accounts payables to contractors and suppliers: 17 percent of total liabilities.
  - Real estate sample: ~470 entities; aggregated total assets: 36 trillion RMB (29 percent of GDP); aggregated total debt: 11 trillion RMB (9 percent of GDP).
  - Around 72 percent of property developers (in terms of assets) have defaulted on their bonds (mostly offshore bonds).
  - Bank lending to property developers: 3 percent of banking sector assets at end-2023.
  - Many developers have had aggregate operating losses since 2021; earnings insufficient to cover interest expenses.
  - If forced liquidation at below-book prices, many developers could see common equity become negative.
- Risks and transmission:
  - Solvency risk is material and could increase with further property price declines.
  - Contagion risk to banks and business partners if unfinished housing problem persists or financial stress spreads.
  - Offshore bond defaults so far have not directly impacted onshore financial system; regulatory forbearance has temporarily limited stress.
- Policy actions for property sector:
  - Accelerate restructuring and/or orderly exits for developers while ensuring delivery of pre-sold housing.
  - Ensure completion of unfinished pre-sold housing.
  - Modernize bankruptcy framework and use hybrid restructuring techniques.
  - Establish contingency planning and market-wide liquidity support; maintain and enforce prudential norms; strengthen provisioning.

### FOREIGN DIRECT INVESTMENT (FDI) TRENDS AND FRAGMENTATION VULNERABILITY
- Recent inward FDI decline (end-2021 to 2023):
  - Actual inward FDI fell by 1.6 pp of GDP.
  - Attribution of the 1.6 pp decline (out-of-sample regression):
    - 0.6 pp of GDP to increase in geopolitical risk.
    - 0.4 pp of GDP to rise in economic policy uncertainty.
    - 0.3 pp of GDP to worsening growth prospects.
    - 0.1 pp of GDP to interest rate differential.
  - Regression explains ~75 percent of cumulative FDI decline since 2021Q4.
- Sectoral patterns (fDi Markets data, Jan 2003–Dec 2023):
  - Manufacturing share of FDI declined from 70 percent in 2017-19 to 60 percent in 2022-23.
  - Manufacturing FDI value: lower by about 70 percent on average between 2022-23 and 2015-19.
  - Strategic manufacturing decline: 81 percent in value terms between 2022-23 and 2015-19.
  - Declines mainly from U.S. and advanced Asian economies; ICT and services shares rose partly due to manufacturing drop.
- Fragmentation vulnerability assessment:
  - IMF fragmentation vulnerability index dimensions: geopolitical, market power, strategic.
  - China’s geopolitical vulnerability: above the 75th percentile among EMDEs and Asia.
  - Large market size offsets vulnerabilities; overall estimated vulnerability close to EMDE median.
- Outward FDI shifts and connector countries:
  - Announced FDI to Vietnam and Mexico in 2022-23 relative to 2018-19 rose by ~170 percent and 300 percent, equivalent to 2 and 0.5 percent of these countries’ GDP respectively.
  - Ideal point distance (IPD) negatively correlated with FDI changes.
  - Gravity-model/PPML findings:
    - Active FTAs → ~30 percent higher number of projects and 20–30 percent higher annual FDI.
    - Manufacturing-export-oriented EMs → ~40 percent higher number of projects and ~25 percent higher annual value.
    - Higher domestic EPU associated with higher outward FDI (2022 EPU increase associated with ~20 percent increase in outward FDI value).
- Policy recommendations:
  - Open up economy to more inward FDI; reduce economic policy uncertainty and mitigate geopolitical risks.
  - Continue pro-market structural reforms to boost productivity and attract FDI.
  - For EMDEs seeking Chinese FDI: diversify exports toward manufacturing; increase trade linkages with China (FTAs); position as geopolitically aligned or connector countries.

*Source: IMF staff analysis in the provided content unit.*

### References _____________________________________________________________________________ 11

### DEFLATION VULNERABILITY AND INFLATION-AT-RISK

### Introduction
- Recent shocks including the property sector adjustment and the pandemic have produced disinflationary pressures and low inflation in China.
- The downward trend in core inflation began before the pandemic and continued through the property market correction and debt overhang.
- Monthly core inflation prints since the pandemic have ranged between 0.4 and 1.3 percent, with a fatter left tail, compared to a pre-pandemic range of 1 to 2.5 percent.
- Weak commodity prices also contributed to declines in the producer price index, headline consumer prices, and the GDP deflator.
- The paper focuses on downside risks to inflation and develops a deflation vulnerability index (DVI) to quantify tail risks.

### Deflation Vulnerability Index: Methodology and Data
- The DVI builds on five categories of indicators from prior literature: commodity prices, output gaps, financial asset prices, exchange rates, and inflation expectations, with refinements for China:
  - Pork prices added to commodity group.
  - China’s secondary market house price (based on 70 cities) and household survey inflation expectations included.
  - Money supply indicator excluded due to high endogeneity.
- Construction method:
  - Principal component analysis (PCA) is applied within each group to derive deflation vulnerability factors (DVF) based on the first two principal components.
  - A second-stage PCA on the five DVFs generates the single DVI summarizing all information.
- Text Table 1 (summary statistics and weights) reports group and variable-level weights and statistics (as in source).
- Historical assessment:
  - Since 2003, China experienced multiple episodes with DVI below zero.
  - The most recent episode beginning in 2020 is the longest recorded period of downward pressure on inflation.
  - Drivers evolved from falling asset prices in 2021–22 to increasingly negative output gaps; in 2023, falling food prices and inflation expectations added to the pressure.

### Inflation-at-Risk (IaR) Findings
- Framework:
  - Local projection quantile regressions estimate the impact of DVI on the h-month ahead distribution of core inflation: π_{t+h,τ} = β_{h,τ} DVI_t + γ_{h,τ} X_{t-1} + ε_t, with emphasis on τ = 10 (10th percentile).
  - Controls include past 12-month average core inflation and year-on-year growth in M2.
- Impact magnitudes and distributional effects:
  - A one-unit increase in DVI is associated with around 1.1 percentage point decline in core inflation at the 10th percentile of the 9-month ahead distribution.
  - The impact on the left-tail (10th percentile) significantly exceeds impacts at the median (50th percentile) and 90th percentile.
- Contributions by component:
  - Output gaps and inflation expectations have the highest predictive power for future deflation risks when the five DVFs are included jointly.
  - A one-standard-deviation negative shock to output gaps reduces the 10th percentile of 9-month ahead core inflation by around 0.6 percentage points.
  - A one-standard-deviation negative shock to inflation expectations reduces the 10th percentile of 9-month ahead core inflation by around 0.4 percentage points.
  - Negative shocks to inflation expectations predict downside risk at the 10th percentile but are irrelevant at the median and 90th percentile.
- Probabilities of deflation:
  - Under the current baseline projection, the probability of 9-month ahead core deflation is around 7 percent.
  - With a 1-standard-deviation shock to DVI, the 9-month ahead chance of deflation increases to 27 percent.
  - With a 2-standard-deviations shock to DVI, the 9-month ahead chance of deflation increases to 54 percent.
- Distributional shift:
  - A negative shock to DVI shifts the entire future core inflation distribution leftward and fattens the left tail, increasing downside risk disproportionally.

### Policies and Conclusion
- Macroeconomic risks:
  - The risk of deflation threatens the strength of China’s post-pandemic recovery and could imply subdued growth and worsening debt dynamics with significant global spillovers.
  - Nominal GDP growth has been below real GDP growth for 4 quarters starting in 2023Q2, an unprecedented pattern for China with implications for debt dynamics, household interest income, and firm profitability.
- Policy recommendations:
  - Prioritize measures to contain macroeconomic impact of the property market adjustment to reduce negative output gaps and support inflation expectations:
    - Stabilize the property market, complete unfinished pre-sold housing, and restructure distressed developers to restore consumer confidence and revive aggregate demand.
  - Use fiscal policy support targeted to lower-income households, who have the highest marginal propensity to consume, to boost aggregate demand.
  - Avoid excessive reliance on supply-side measures to expand productive capacity amid weak demand, as this could exacerbate deflationary pressures.
  - The People’s Bank of China should further ease the monetary policy stance and enhance monitoring of deflation risks, focusing on anchoring inflation expectations and closing the negative output gap.
  - Strengthen monitoring of downside risks via inflation-at-risk analysis and improve measurement and monitoring of inflation expectations.
  - Strengthen central bank communication to guide market inflation expectations over a longer horizon.

*Source: IMF staff analysis in "DEFLATION VULNERABILITY AND INFLATION-AT-RISK" (content unit).*

### References

### References

### Citations
- Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone, 2019. “Vulnerable Growth.” American Economic Review. 109(4): pp. 1263-89.  
- Adrian, Tobias, Federico Grinberg, Nellie Liang, Sheheryar Malik, and Jie Yu, 2022. “The Term Structure of Growth-at- -Risk.” American Economic Journal: Macroeconomics. 14(3): PP. 283-323.  
- Chen, Sally, and Joong Shik Kang, 2018. “Credit Booms—Is China Different?” IMF Working Paper 2018/002.  
- Decressin, Jörg, and Douglas Laxton, 2009. “Gauging Risks for Deflation.” IMF Staff Position Note. 2009.  
- Kumar, Manmohan, Taimur Baig, Jörg Decressin, Chris Faulkner-MacDonagh, and Tarhan Feyzioglu, 2003. “Deflation: Determinants, Risks, and Policy Options.” IMF Occasional Paper 221.  
- López-Salido, David, Francesca Loria, 2024. “Inflation at risk,” Journal of Monetary Economics. In press.  
- International Monetary Fund, 2024a. People’s Republic of China: 2023 Article IV Conclusion Staff Report.  
- International Monetary Fund, 2024b. People’s Republic of China: 2024 Article IV Conclusion Staff Report.  

### Annex I. Data Sources and Sample Description — Underlying data for China’s Deflation Vulnerability Index
- Monthly Variables (variable — description — sample dates — source):  
  - Rice price inflation — Grain price y/y percent change. Jan 2000-Feb 2024 — NBS, Haver  
  - Pork price inflation — Pork price y/y percent change. Feb 2005-Feb 2024 — NBS, Haver  
  - Fuel price inflation — Fuel and power price y/y percent change. Jan 2000-Feb 2024 — NBS, Haver  
  - Stock price returns — Market total return index y/y percent change. Jan 2000-Feb 2024. — Datastream  
  - House price returns — Secondary market residential price across 70 cities, y/y percent change. Jan 2011-Feb 2024 — CEIC  
  - Exchange rate (vs. USD) — Nominal exchange rate per USD, y/y percent change. Jan 2000-Feb 2024. — Haver  
  - Real Effective Exchange Rate — Narrow REER: CPI-based. Jan 2000-Feb 2024. — Bruegel, Haver
- Quarterly Variables (variable — description — sample dates — source):  
  - Output gaps — Output gap to GDP ratio in percent. 2000Q1-2023Q4. — IMF staff estimation  
  - Inflation expectations — Consumer Price Expectation for the Next Quarter (50+ = positive view), y/y percent change. 2000Q1-Feb 2023Q4. — NBS, Haver

### Service Sector Productivity, Allocative Efficiency and Innovation — Key findings and structure
- Paper focus: examines the role of the service sector in driving productivity growth historically and the extent service sector reforms can support future productivity and growth.
- Main conclusions (high-level):  
  - Structural transformation positively contributed to economy-wide total factor productivity (TFP) growth and innovation in the last 20 years in China.  
  - Decline in value added share of the secondary sector was led by low TFP subsectors, such as oil and gas.  
  - TFP growth of market services exceeded that of the secondary sector.  
  - There is substantial and growing misallocation of capital and labor in the service sector, masked by high innovation.  
  - Further opening up to trade, greater labor mobility, and state-owned enterprise (SOE) reforms could improve allocative efficiency and boost TFP growth, particularly in services.

### A. Introduction — Context and policy priorities
- China’s real GDP growth is expected to slow; search for new engines of growth is a priority.  
- Growth driven by the property sector has become unsustainable, with the fundamental demand for housing expected to decline significantly (IMF, 2024).  
- Manufacturing gains from trade may be dampened by growing fragmentation pressures.  
- Promoting sustainable growth requires rebalancing towards consumption and structural reforms to boost falling productivity growth and mitigate drag from a declining labor force.  
- Service sector can be a source of sustainable growth going forward.

### B. Stylized Facts — Size, composition, and heterogeneity
- Service sector growth: rose from under 30 percent to over 50 percent of China’s nominal value added over four decades, nearly surpassing the share of the secondary sector.  
- Services value-added share in advanced economies is about 20 percentage points above that in China.  
- The secondary sector began gradually declining after 2011 in nominal value-added share terms.  
- Heterogeneity across subsectors:  
  - Non-market services (healthcare, education) tend to have low or negative TFP growth and levels.  
  - Market service subsectors generally have positive TFP growth and levels, except for real estate services.  
  - Within the secondary sector, most manufacturing subsectors have positive TFP levels and growth, but some with significant state ownership (e.g., oil & gas, petroleum) had negative TFP levels and growth during 2003-2019.

### C. Rebalancing Towards Services — Contributions to aggregate productivity
- Two channels for aggregate productivity growth: within-sector productivity increases and structural change (reallocation of labor and capital).  
- Non-market service expansion typically does not contribute to aggregate TFP growth (Baumol, 1967).  
- Market services (e.g., IT, financial services) have high TFP growth and TFP levels; their expansion can positively contribute to aggregate TFP growth.  
- Shift-share analysis results:  
  - Structural change has positively contributed to aggregate TFP growth throughout the last two decades.  
  - Positive contribution from shifting to market services was particularly strong in the 2000s and remained positive in the 2010s but faded somewhat due to expansion of real estate services, which contributed negatively to overall TFP growth of market services.  
  - Even including low-productivity non-market services, rebalancing towards services overall has not impacted China’s aggregate TFP adversely.  
- Empirical decomposition: contributions shown for periods including 1988-2003, 2004-2017 and subperiods 2004-11, 2011-17 (charts and calculations reported in source).

### D. Allocative Efficiency and Innovation — Mechanisms driving TFP
- Definitions and measurement:  
  - Allocative efficiency: how well capital and labor are allocated across firms; measured via dispersion in revenue productivity of capital and labor across firms (Hsieh and Klenow (2009); Bils et al. (2021)).  
  - Innovation: technological and process innovation within firms raising firm TFP.  
  - Data: Orbis data on value added, workforce and capital stock for around 40,000 firms used to calculate allocative inefficiency.  
- Key empirical points:  
  - Market services in China have experienced growing misallocation of capital and labor despite relatively strong TFP growth, which masks high underlying technological innovation.  
  - Declining allocative efficiency implies capital and labor increasingly concentrated in relatively unproductive firms. Potential frictions include preferential credit and subsidies to SOEs, regulatory barriers to market entry, limitations on nationwide operation, and labor mobility constraints (household registration system).  
  - Innovation in China took off around 2015 across all sectors, most prominently in market services; the 2015 introduction and rapid adoption of mobile-payment apps and other IT advances likely spurred innovation in services.  
  - It is possible to have both high innovation and high allocative inefficiency in new, dynamic sectors where young firms grow productivity rapidly but attract capital and labor slowly, causing temporary allocative inefficiency.  
- Empirical metrics and trends reported:  
  - Negative growth in allocative efficiency in market services since 2003.  
  - Rising innovation led by the market services sector, which has offset the negative allocative efficiency contribution in aggregate TFP growth.

### E. Policy implications (as presented)
- Reforms to improve allocative efficiency in market services could lift aggregate TFP growth further. Key policy directions identified in the analysis:  
  - Further opening up to trade.  
  - Greater labor mobility (addressing household registration system frictions).  
  - State-owned enterprise (SOE) reforms to reduce preferential credit/subsidies and level the playing field.  
  - Reducing regulatory barriers that prevent market entry or nationwide operation of new firms.

*Source: 1chnea2024004-print-pdf - References*

### 14.      While China’s allocative efficiency is low compared with major advanced economies in

### While China’s allocative efficiency is low compared with major advanced economies in all sectors, the gap is the largest in some market services

### Key findings on allocative efficiency and sectoral patterns
- The allocative efficiency gap measures China’s implied TFP loss from greater measured resource misallocation relative to an average of five major AEs (DEU, FRA, JPN, ITA, USA; weighted by PPP GDP).
- The allocative efficiency gap is:
  - Smallest in construction and mining, indicating China’s allocative efficiency is similar to advanced economies in these sectors.
  - Largest in market services, particularly hospitality, retail and information services.
- Across countries, allocative efficiency tends to be lower in services than in goods-producing sectors; misallocation in China’s market services is especially pronounced.
- Time periods and decomposition noted in the analysis include 2004-17, 2004-11, and 2011-17 contributions to aggregate allocative efficiency growth (excluding non-market service sectors), and analogous decompositions for aggregate innovation.

### Quantitative and sectoral indicators
- Share of SOEs in sectoral value added, 2017:
  - Service sector: 34 percent
  - Industry: 21 percent
- Firm-level measure:
  - As of 2018, the average SOE’s revenue productivity in both manufacturing and services was about 3 percent below the average of private owned companies (POEs). Revenue productivity gaps are reported in log points with 95% confidence intervals.
- Service Trade Restrictiveness Index, 2022 (simple average across 22 service sectors):
  - China appears less open than many comparator countries; subcomponents indicate higher restrictions on foreign entry, less regulatory transparency, and more barriers to competition relative to the world average.
- Cross-country and cross-sector evidence:
  - The analysis uses a structural component of allocative efficiency obtained from dynamic panel regressions (“structural allocative efficiency”) and covers a sample comprising 20 economies and 5 market services for specific sectoral regressions.
  - For market-services sectors across 20 major economies (2019), there is a negative correlation between service-sector openness (trade/gross output = sum of exports and imports divided by sector gross output) and allocative efficiency gaps; China’s market services are highlighted as having both relatively high misallocation and low openness.

### Drivers of low allocative efficiency in Chinese market services
- Two specific factors contribute materially to low allocative efficiency:
  - High trade restrictions in services, as reflected in the OECD Service Trade Restrictiveness Index and its subcomponents (restrictions on foreign entry; regulatory transparency; barriers to competition; restrictions to movement of people; other discriminatory measures).
  - The presence and scale of SOEs, especially pronounced in several service sectors (finance, transport, storage, post and communications), which raise the average share of SOEs in services sectoral value added.
- Firm-level regressions indicate SOEs tend to overemploy capital and labor and exhibit lower revenue productivity than POEs.

### Policy analysis and evidence on reforms
- Cross-country and case-study evidence:
  - More competitive and open markets are associated with higher allocative efficiency across 20 major economies using indices on barriers to competition and barriers to international trade.
  - Historical examples: removing credit market distortions in Chile during the 1980s boosted TFP by reducing misallocation (Chen and Irarrazabel, 2015); trade liberalization raised allocative efficiency in Vietnam in the 2000s (Ha and Kiyota, 2016).
- Sector-level implication:
  - For 6 market-services sectors, lower trade openness correlates with larger allocative efficiency gaps relative to major AEs (2019 observations).
  - In a large country like China, reducing barriers to cross-provincial trade could significantly improve allocative efficiency.

### Policy recommendations to lift allocative efficiency and productivity
- Reduce restrictions to trade in services and interprovincial barriers to trade to expose service firms to greater competition and market discipline.
- Pursue SOE reform to allow capital and labor “stuck” in lower-productivity SOEs to reallocate to more productive POEs; evidence suggests potential gains given SOEs’ average revenue productivity was about 3 percent below POEs as of 2018.
- Allow greater business dynamism:
  - Facilitate greater firm entry and exit across a unified national market.
  - Scale back industrial policies that implicitly or explicitly favor specific companies (e.g., SOEs) or sectors.
- Continue reforms in the household registration system (Hukou) to improve labor mobility.
- Maintain and deepen ongoing regulatory liberalization efforts (examples noted: reduction of FDI restrictions and shareholding restrictions in some financial services; pilot and national free trade zone lists specifying service sectors accessible to foreign entities).

### Aggregate implications and conclusion
- Rebalancing toward market services has contributed positively to growth, but market services remain relatively small for China’s level of development and are an underexploited driver of growth.
- High innovation among firms in market services has so far largely offset low allocative efficiency; reducing impediments to market services development could unlock sizeable productivity and income gains.
- Greater trade openness and SOE reforms would improve allocative efficiency and unleash higher TFP growth, especially as the service sector expands with domestic consumer preferences and as China converges to advanced-economy income levels.
- With continued innovation and policy reforms that improve allocative efficiency, market services can be a sustainable source of growth and help reduce youth unemployment given the sector’s employment role.

*Source: IMF staff analysis in Chapter on China’s allocative efficiency and market services (selected textual extracts and figures).*

### 2.      China’s current intensity-based ETS allocates free allowances based on four fuel- and

### 2.      China’s current intensity-based ETS allocates free allowances based on four fuel- and technology-specific benchmarks for coal and gas power plants

### Overview of the intensity-based ETS (IB-ETS)
- Each fossil fuel power plant receives annual carbon emissions allowances equal to: (benchmark for their technology class) × (production in that period).
- Coal-fired power generators have higher benchmarks and receive more allowances.
- Plants with emissions intensity higher than their benchmark must reduce emissions intensity or purchase allowances from plants with emissions intensity lower than their benchmark.
- The IB-ETS incentivizes improvements in emissions intensity via retrofits, shifting generation to more efficient plants, and use of carbon capture, utilization and storage (CCUS).
- If aggregate production rises, the total number of carbon emissions permits in the system increases—unlike a quantity-based ETS (QB-ETS) which places an economy-wide cap on total emissions.

### Institutional context and planned reforms
- National IB-ETS introduced in 2021; planned reforms include:
  - Extension of ETS coverage to the industrial sector.
  - Introduction of auctioning of allowances (specific details and timeline not yet confirmed).
- Complementary policies:
  - Renewables policy via Five-Year Plans focused initially on installed capacity targets; recent emphasis on renewables integration and consumption targets.
  - Construction of China Certified Emission Reduction Trading System (CCER), a voluntary carbon market to supplement the ETS.
- By 2025 targets cited:
  - non-fossil electricity generation to reach 39%
  - renewable electricity consumption to account for 33% of total electricity consumption
  - 30 GW of new energy storage capacity by 2025 (primarily battery systems)
  - 120 GW of total pumped hydro storage by 2030

### Key benchmark projections (Table 1, benchmark units: tons of CO2 per megawatt-hour)
- Conventional coal-fired units >400MW (Subcritical > 400MW; Supercritical > 400 MW; Ultrasupercritical; Coal + CCS):
  - 2022: 0.8177
  - 2025: 0.6549
  - 2030: 0.5420
  - 2035: 0.6476
  - % Reduction by 2030: -33.7%
- Conventional coal-fired units ≤400MW (High-pressure Subcritical <= 400MW; Supercritical <= 400MW):
  - 2022: 0.8729
  - 2025: 0.6991
  - 2030: 0.5785
  - 2035: 0.6914
  - % Reduction by 2030: -33.7%
- Unconventional coal-fired units (Circulating fluidized bed (CFB)):
  - 2022: 0.9303
  - 2025: 0.7451
  - 2030: 0.6166
  - 2035: 0.7368
  - % Reduction by 2030: -33.7%
- Gas-fired units (Gas; Gas + CCS):
  - 2022: 0.3901
  - 2025: 0.3124
  - 2030: 0.2586
  - 2035: 0.3090
  - % Reduction by 2030: -33.7%

### Reform scenarios (all calibrated to NDC-consistent emissions path)
- Shared assumptions:
  - Future emissions path consistent with China’s NDCs: peak carbon before 2030 and cut carbon intensity of GDP by 65 percent from 2005 levels by 2030.
  - NDC-Consistent path implies power sector emissions of 4.9 billion metric tons by 2030 (a reduction of 15 percent).
  - Equal contributions to emissions reduction from each sector; emissions taper at same rate beyond 2030.
- Scenarios considered:
  - Baseline IB-ETS (no reforms): 4 benchmarks projected to cumulatively tighten by 34 percent between 2023 and 2030 to achieve NDC goals.
  - Scenario 1: Partial auctioning — share of allowances auctioned grows linearly from 1 to 25 percent in 2030.
  - Scenario 2: Quantity-based ETS (QB-ETS) — cap on total quantity of emissions rather than intensity benchmarks.
  - Scenario 3: Expand IB-ETS’ coverage to industry:
    - Phase 1: include cement, aluminum, iron and steel.
    - Phase 2: include pulp and paper, other non-metal products, other non-ferrous metals, chemicals, petroleum refining.

### Modeling approach and key assumptions (CPAT + ETS power plants module)
- Models all Chinese fossil fuel plants at province-technology class level.
- Plants make profit-maximizing decisions on quantity and fuel efficiency (heat rate).
- Includes administrative pricing arrangements (fixed-price contracts between plants and state-owned entities).
- Firms shut down if unprofitable for three consecutive years.
- Partial pass-through of costs to electricity prices is assumed.
- Coal price made endogenous to Chinese demand.
- Renewable generation share in 2030 rises from 37 percent to 44 percent when IB-ETS is considered.
- Benchmark treatment after 2030: benchmarks allowed to loosen up to 2035 to avoid overly restrictive cuts.

### Key results and comparisons for 2030 (from Text Table 2; all figures represent annual amounts or percentage changes for the year 2030 only)
- Change in Electricity Price from 2019:
  - No Policy: 28%
  - Baseline: Intensity-based ETS: 38%
  - Auction Permits: 41%
  - Quantity-based ETS: 45%
  - Include Industry, Phase 1: 35%
  - Include Industry, Phase 2: 34%
- Government Revenues, % of GDP:
  - No Policy: 0%
  - Baseline: Intensity-based ETS: 0.23%
  - Auction Permits: 0.33%
  - Quantity-based ETS: 0.25%
  - Include Industry, Phase 1: 0.20%
  - Include Industry, Phase 2: 0.20%
- Welfare Gain, Relative to No Policy, % of GDP:
  - No Policy: 0.0%
  - Baseline: Intensity-based ETS: 0.43%
  - Auction Permits: 0.44%
  - Quantity-based ETS: 0.49%
  - Include Industry, Phase 1: 0.56%
  - Include Industry, Phase 2: 0.56%
- Other variables (2030):
  - Power Sector Emissions Reduction:
    - No Policy: 0%
    - Baseline IB-ETS: -15%
    - Auction Permits: -15%
    - QB-ETS: -15%
    - Include Industry, Phase 1: -10%
    - Include Industry, Phase 2: -8%
  - Production from Fossil Fuel Power Plants (millions of GJ):
    - No Policy: 19,648
    - Baseline IB-ETS: 17,271
    - Auction Permits: 17,268
    - QB-ETS: 17,173
    - Include Industry, Phase 1: 18,274
    - Include Industry, Phase 2: 18,586
  - Firm Profits, Relative to No Policy:
    - No Policy: 0.0%
    - Baseline IB-ETS: -26.3%
    - Auction Permits: -34.1%
    - QB-ETS: -18.2%
    - Include Industry, Phase 1: -6.0%
    - Include Industry, Phase 2: -3.7%
  - Emissions Intensity (mtons CO2/GJ):
    - No Policy: 0.291
    - Baseline IB-ETS: 0.283
    - Auction Permits: 0.283
    - QB-ETS: 0.284
    - Include Industry, Phase 1: 0.282
    - Include Industry, Phase 2: 0.282
  - Needed Change in Benchmark:
    - No Policy: N/A
    - Baseline IB-ETS: -35%
    - Auction Permits: -35%
    - QB-ETS: N/A
    - Include Industry, Phase 1: -12%
    - Include Industry, Phase 2: -8%

### Interpretation of results
- All reform scenarios targeting the same NDC-consistent emissions path raise welfare relative to No Policy; extending IB-ETS to industry yields the largest welfare gains.
- QB-ETS reduces fossil fuel electricity production more than IB-ETS but raises electricity prices the most.
- Auctioning permits raises electricity prices more than baseline IB-ETS but generates fiscal revenues that can be used for transfers or mitigation of competitiveness impacts.
- Extending IB-ETS to industry reduces the burden on the power sector, resulting in smaller increases in electricity prices compared with auctioning or QB-ETS, but raises fossil fuel electricity production and emissions relative to baseline IB-ETS.
- Revenues from auctioning (Scenario 1) estimated around $40 billion by 2030, or 0.2 percent of GDP; revenues can be used for general budget purposes, transfers to low-income households, or addressing competitiveness issues.

### Welfare analysis specifics
- Total welfare change = change in consumer surplus + change in producer surplus + government revenues.
- Environmental benefit valuation added: difference between social cost and supply cost set at $0.06 per kWh for coal-fired electricity and $0.12 per kWh for gas-fired electricity (as estimated in Black and others, 2023).
- Although QB-ETS raises electricity prices the most, lower costs to power plants offset consumer losses and produce net welfare gains.
- IB-ETS with permit auctioning raises most revenue but yields the least improvement in consumer and producer surplus relative to baseline IB-ETS.
- Across 2023–2035 aggregation, all reform scenarios dominate the baseline IB-ETS in combined consumer/producer surplus plus government revenues.

### Distributional consequences and mitigation
- Electricity price increases raise costs for households and firms; poorer households are disproportionately affected due to higher expenditure shares on electricity and electricity-intensive goods.
- QB-ETS scenario produces the largest decreases in household incomes across deciles because it raises electricity prices the most.
- Targeted lump-sum transfers can offset regressivity:
  - Example modeled: directing 10 percent of new government revenues as lump-sum transfers to households in the lowest 3 deciles reverses regressivity—poorest households can benefit under IB-ETS and the three reform scenarios.
- Firm input costs increase broadly, mostly between 0.5 and 2.5 percent; largest increases in energy-intensive industries (e.g., aviation).
- Input cost increases for cement, iron and steel, and other metals remain limited until those sectors are included in industry IB-ETS (Scenario 3).
- For firms projected to shut down (e.g., some coal power plants), temporary local and worker support recommended; but direct transfers to low-income households and strengthening social safety nets (including unemployment insurance) are preferred for addressing distributional concerns.

### Conclusions and policy recommendations
- Extending coverage of the IB-ETS to industrial firms as soon as possible would help China reach climate goals at lower overall cost; moving from 44 percent to almost 80 percent of China’s total CO2 emissions coverage is recommended.
- Use the ETS to raise revenue (partial auctioning of permits) and deploy revenues to provide transfers to low-income households, reversing the regressivity of mitigation policies.
- A gradual transition to a quantity-based ETS (QB-ETS) with an economy-wide emissions cap would maximize social welfare gains over time.
- Reaccelerate power sector reforms (more market-based electricity pricing and greater price flexibility) to improve responsiveness to market signals and increase ETS benefits.
- Early climate mitigation action—before 2035—is critical to reduce overall costs of reaching China’s 2060 carbon neutrality goal and to contribute to global warming limitation targets.

*Source: IMF staff analysis in chapter section 2 of the provided content unit.*

### 20.      China’s existing plans to extend the intensity-based ETS to industrial firms should be

### 1chnea2024004-print-pdf - 20.      China’s existing plans to extend the intensity-based ETS to industrial firms should be

### Accelerate extension of the intensity-based ETS to industrial firms (Scenario 3)
- Recommendation: China’s existing plans to extend the intensity-based ETS (IB-ETS) to industrial firms should be accelerated (Scenario 3).
- Rationale:
  - Spreading emissions reductions from the power sector (44 percent of total emissions) to include the industrial sector (an additional 34 percent) would spread the burden of reducing a given amount of emissions.
  - The gains from a smaller emissions reduction in the power sector outweigh losses from emissions reduction in the industrial sector, thus improving welfare.
- Welfare and prices:
  - Scenario 3 indicates that electricity price increase would be lower when the IB-ETS extends fully to the industrial sector than under the baseline of existing IB-ETS.

### Use the ETS to raise fiscal revenue and protect low-income households
- Policy recommendation:
  - Use the ETS to raise fiscal revenue and use it to provide transfers to low-income households.
  - Gradually introduce permit auctioning and reduce the share of free permits as a way to raise revenue.
- Distributional impacts:
  - In all scenarios considered, China experiences higher electricity prices as it reduces fossil fuel emissions in the energy sector.
  - These higher electricity prices reduce real incomes in poor households more than those in rich households.
  - Targeted transfers toward the lowest income deciles would reverse the regressivity of each policy.

### Medium-term consideration: switch to a quantity-based ETS (Scenario 2)
- Recommendation: Over the medium-term China should consider switching to a quantity-based ETS (QB-ETS) (Scenario 2) with a clear cap on total emissions.
- Supporting arguments:
  - This is consistent with environmental economics theory and prior modeling from Goulder and others (2022).
  - Although the QB-ETS would increase energy prices for households and costs for firms, it would generate new government revenues that can be used to mitigate the harmful effects of these costs.
  - As renewable energy generation capacity and electricity storage options improve, moving to a QB-ETS, while also extending coverage, would maximize welfare while preserving China’s energy security.
- Implementation note:
  - Switching to a QB-ETS is equivalent to either auctioning all permits or dropping emissions intensity benchmarks to zero in the IB-ETS. See Annex for details.

### CPAT model and power plants module: scope and timeline
- Model overview:
  - The Climate Policy Assessment Tool (CPAT) is a flexible spreadsheet model developed by IMF and World Bank staff.
  - CPAT provides consistent cross-country projections for 200 countries of fuel use and CO2 emissions by major energy sectors and the emissions, fiscal, economic, and distributional impacts of carbon pricing and other mitigation instruments.
  - CPAT covers the period between 2020 and 2035.
- Power plants module:
  - A model of China’s power sector was built and integrated into CPAT, modeling fossil fuel power plants at the province and technology class level.
  - Data include power plants at the province and technology class level; baseline calibrated for Chinese fossil fuel plant production in 2019.
  - CPAT uses international data sources to project the price of electricity, coal, and natural gas in future years and projects investment and capacity additions, determining renewable energy capacity trajectories.

### Profitability and optimization under IB-ETS and QB-ETS
- Firm behavior under IB-ETS:
  - Firms optimize profits with respect to quantity of power produced (q) and heat rate (h) under an IB-ETS profitability equation that includes:
    - Revenue terms for administrative contracts (p̅ and q̄) and market sales (p and q).
    - Production cost terms with parameters φ0, φ1, φ2; fuel cost pf; heat rate h; energy per unit fuel ξ.
    - IB-ETS compliance cost term with permit cost t, carbon content ψ, and government-set emissions benchmark β.
    - Cost of adjusting heat rate governed by γ and α, with initial heat rate h0.
  - Optimization produces first-order conditions relating p, pf, t, ψ, β, γ, α, h0, ξ and q.
- Firm behavior under QB-ETS:
  - Under a QB-ETS, the compliance cost term is t h ψ q (permit price times actual emissions), and firms optimize q and h with corresponding first-order conditions.
  - A power plant under an IB-ETS has the same profits function as under a QB-ETS if the emissions benchmark β in the IB-ETS were set to zero; this illustrates the transition path.

### Permit price dynamics, capping, and fiscal implications
- Two IB-ETS implementation methods and outcomes:
  - Method 1 (floating permit prices, all permits allocated for free):
    - If permit prices are allowed to float with all permits allocated for free, permit prices become unrealistically high, crossing a level of $800 per ton of CO2 in 2030 and almost $2,000 per ton of CO2 in 2035.
    - Overall costs for power firms would increase by a factor of 3 to 5, necessitating massive increases in the price of electricity to avoid bankrupting fossil fuel firms.
  - Method 2 (cap permit prices, relax constraint on number of permits sold):
    - Cap permit prices while relaxing the constraint on the number of permits sold in the market.
    - The overall number of permits is the same in both modelling approaches, determined by the same NDC-consistent emissions path for the power sector.
    - Fewer permits allocated for free, remainder sold by government in the market at capped prices.
    - Tighter benchmarks imply a greater portion of the reduction in emissions comes from cuts in the quantity of power rather than reductions in emissions intensity.
    - Government sales of permits at capped prices generate some fiscal revenue even without permit auctioning.

### Calibration, scenarios, and renewable growth mechanics
- Scenario calibration:
  - QB-ETS scenario calibrated using CPAT’s projection for a QB-ETS in China; CPAT built to model carbon pricing instruments such as a QB-ETS.
  - IB-ETS scenario calculated by finding a permit price and path for emissions intensity benchmarks so that emissions from fossil fuel plants meet an NDC-consistent level in 2030 while production matches projected production under IB-ETS power prices in CPAT.
- Renewable energy growth:
  - CPAT calculates the growth in renewable power starting with existing capacity in historical years in China, estimating generation via historical utilization factors and dispatching carbon-free sources first; remaining demand is filled by fossil fuels (primarily coal).
  - Investment decisions for future periods compare total cost of electricity generation by fuel; resulting investment determines renewable capacity in following years.

*Source: Extracts from the chapter/section in the provided IMF PDF content.*

### 7.      The decline in FDI inflows since end-2021 is estimated to have been primarily driven

### 7.      The decline in FDI inflows since end-2021 is estimated to have been primarily driven

### Drivers of the inward FDI decline (end-2021 to 2023)
- Actual inward FDI fell by 1.6 pp of GDP over this period.
- Out-of-sample regression attribution of the 1.6 pp decline:
  - 0.6pp of GDP to the increase in geopolitical risk.
  - 0.4 pp of GDP to the rise in economic policy uncertainty.
  - 0.3 pp of GDP to worsening growth prospects.
  - 0.1pp of GDP to the interest rate differential.
- The regression explains about 75 percent of the cumulative FDI decline since 2021Q4.
- Lagged EPU and lagged GPR have statistically significant estimated effects in the inward FDI regression (EPU, lagged: -0.003*** and -0.002*** in reported specifications; GPR, lagged: -1.738*** in reported specification).
- Other model findings noted:
  - The estimated role of the interest rate differential is small (0.1pp of GDP), despite incentives to move earnings offshore to take advantage of higher interest rates abroad.
  - VIX and expected US real growth enter the specifications but are not the primary drivers in the regression attribution above.

### Sectoral patterns and private-data augmentation
- Official BOP-based FDI data lack detailed sector breakdowns and country-of-origin clarity for Mainland China (dominated by Hong Kong SAR and British Virgin Islands that often channel investments).
- The analysis complements official data with fDi Markets proprietary data (January 2003–December 2023) on bilateral gross greenfield FDI flows (source, destination, sector, USD volume).
- Key sectoral findings from fDi Markets:
  - FDI into China is concentrated in manufacturing, including strategic manufacturing.
  - The share of manufacturing FDI declined from 70 percent in 2017-19 to 60 percent in 2022-23 (fDi markets data).
  - Lower manufacturing FDI in 2022-23 declined by about 70 percent in value terms on average between 2022-23 and 2015-19.
  - For strategic manufacturing, the decline was 81 percent in value terms between 2022-23 and 2015-19.
  - The reduction in inward FDI was primarily due to declines in flows from the U.S. and advanced Asian economies; ICT and services shares rose partly reflecting the drop in manufacturing and North American flows.

### Fragmentation vulnerability and China’s position
- IMF fragmentation vulnerability index (three dimensions: geopolitical, market power, strategic) is used to assess future fragmentation risks to inward FDI.
  - Geopolitical vulnerability: higher when a larger share of investments comes from geopolitically distant countries.
  - Market power: large market share can offset geopolitical vulnerability since relocation costs for foreign firms rise.
  - Strategic dimension: FDI in strategic sectors faces heightened vulnerabilities irrespective of geopolitical distance.
- China-specific results:
  - Geopolitical vulnerability is above the 75th percentile among EMDEs and Asia.
  - A sizable share of strategic manufacturing creates additional vulnerability.
  - China’s large market size largely offsets these vulnerabilities, yielding an overall estimated vulnerability close to the EMDE median.

### Outward FDI shifts and “connector” countries
- fDi Markets shows China’s outward FDI projects declined substantially to many European and Asian AEs in 2022-23, while flows to non-aligned “connector” countries (notably Mexico, Vietnam) more than doubled.
  - Announced FDI to Vietnam and Mexico in 2022-23 relative to 2018-19 rose by about 170 percent and 300 percent, equivalent to 2 and 0.5 percent of these countries’ GDP respectively.
- Ideal point distance (IPD), a geopolitical distance measure based on UNGA voting, is strongly negatively correlated with these FDI changes.
- Gravity-model/PPML findings for China’s outward FDI:
  - Specifications include destination and China GDP, lagged FDI, labor cost (USD per capita proxy), business start-up costs, and post-2018 interaction terms (tariff era).
  - Geopolitical distance was already negatively associated with number of projects pre-2018; post-2018 interaction terms (about -0.16 in some specifications) indicate an increased reliance on geopolitically aligned countries.
  - Proximity-to-GDP (a CES-aggregated distance-to-market-size measure) findings:
    - Pre-2018: proximity-to-GDP contributed negatively to the number of projects but positively to investment value (implying large-value projects located in large economies; many smaller projects located in connector countries).
    - Post-2018: coefficients indicate even larger investments moved away from larger economies (specification 5 total coefficient on proximity-to-GDP post-2018: -0.23).
  - Physical distance effects:
    - Pre-2018: log distance had a significant negative effect on investment value (e.g., a coefficient around -0.2).
    - Post-2018: positive significant interaction term for distance implies China’s FDI were destined to more distant countries.
  - Policy and structural determinants:
    - Active FTA associated with about 30 percent higher number of projects and 20-30 percent higher annual China’s FDI.
    - Having manufacturing exports as main export earnings significantly boosts annual number of projects by 40 percent and annual value by about 25 percent.
    - EMs reliant on fuels for exports received large China FDI in value (positive coefficient) but fewer projects (negative coefficient), consistent with extraction-focused projects.
    - Higher domestic economic policy uncertainty (EPU) is associated with higher outward FDI: the 2022 EPU increase is estimated to be associated with about a 20 percent increase in outward FDI value (specification 5).

### Conclusions and policy recommendations
- Synthesis of causes and implications:
  - The recent lower inward FDI in China reflects both cyclical and structural factors: weaker growth prospects, elevated economic policy uncertainty, and increased geopolitical risks.
  - Lower FDI inflows could contribute to a vicious cycle of weaker productivity and future economic activity, highlighting the need to reverse the trend.
- Policy recommendations:
  - Open up the economy to more inward FDI (consistent with authorities’ plans).
  - Reduce economic policy uncertainty and mitigate geopolitical risks to minimize negative impacts on foreign investment.
  - Press ahead with pro-market structural reforms to boost productivity and improve growth prospects, which would attract more inward FDI and generate additional positive feedback on growth.
- For EMDEs seeking to attract Chinese FDI:
  - Diversify exports toward manufacturing.
  - Increase trade linkages with China, including by establishing FTAs.
  - Position as geopolitically aligned or “connector” countries to maintain market access for Chinese investors.

*Source: IMF staff calculations and analysis as presented in the chapter.*

### References

### References

### Key citations
- Aiyar S., Habib A., Malacrino D, and Andrea F. Presbitero (2023) “Investing in Friends: The Role of Geopolitical Alignment in FDI Flows”, International Monetary Fund, August 2023
- Bailey, Michael A., Anton Strezhnev, and Erik Voeten. "Estimating dynamic state preferences from United Nations voting data." Journal of Conflict Resolution 61.2 (2017): 430-456
- Conte, M., P. Cotterlaz and T. Mayer (2022), "The CEPII Gravity database". CEPII Working Paper 2022-05, July 2022.
- Davis S., Liu S. and Xuguang S. Sheng (2019) ''Economic Policy Uncertainty in China Since 1949: The View from Mainland Newspapers”
- International Monetary Fund (2022), “The Liberalization and Management of Capital Flows: An Institutional View”, Washington, DC: International Monetary Fund.
- International Monetary Fund (2012), “The Liberalization and Management of Capital Flows: An Institutional View”, Washington, DC: International Monetary Fund.
- Caldara, D., and Matteo Iacoviello (2021), “Measuring Geopolitical Risk,” working paper, Board of Governors of the Federal Reserve Board, November 2021
- Gopinath, G., Gourinchas P., Presbitero A. and Petia Topalova (2024) “Changing Global Linkages: A New Cold War?” IMF Working paper No. 2024/076
- M. C. Santos Silva and, Silvana Tenreyro (2006) “The Log of Gravity”. The Review of Economics and Statistics 2006; 88 (4): 641–658. doi: https://doi.org/10.1162/rest.88.4.641
- Molnar, M., T. Yan and Y. Li (2021), "China’s outward direct investment and its impact on the domestic economy", OECD Economics Department Working Papers, No. 1685, OECD Publishing, Paris, https://doi.org/10.1787/1b1eaa9d-en.

### Summary of substantive findings from "Assessing Vulnerabilities of China’s Corporate Sector" (selected issues text included in this content unit)
- Overall context and objectives
  - The paper assesses China’s corporate sector vulnerabilities after the pandemic, with three objectives: (i) stock-taking of underlying nonfinancial corporate sector vulnerabilities; (ii) examine signs of corporate deleveraging, likely concentrated in highly leveraged sectors; (iii) assess financial stability implications of financially vulnerable corporate entities.
  - Analysis is organized into nonfinancial firms excluding real estate and the real estate sector. Local government financing vehicles (LGFVs) are excluded from the corporate analysis and their debt is included in general government debt per the IMF’s Government Finance Statistics Manual (2014).

- Aggregate corporate debt and financing structure
  - Combined debt of nonfinancial firms and LGFVs: 172 percent of GDP as of end-2024Q1 (BIS data).
  - Combined debt at end-2009Q1: 106 percent of GDP.
  - Excluding LGFV debt, corporate debt: 131 percent of GDP.
  - Financing sources: bank lending 74 percent, bond financing 15 percent, nonbank financing 9 percent.
  - Within bank lending, manufacturing, business services, and transport each account for about 15 percent of bank lending.

- Overall corporate leverage and deleveraging patterns
  - Aggregate ratio total debt to total assets fluctuated around 38 percent; ratio based on total liabilities stood at 66 percent in 2023.
  - Net debt (debt net of cash) relative to EBITDA edged up due to more moderate earnings growth compared to debt growth.
  - Central state-owned enterprises (SOEs) have the highest corporate leverage and indebtedness; they enjoy better financing access and lower funding costs.
  - Share of firms with negative common equity has been close to zero (immediate solvency risk appears limited).
  - Slightly more than half of firms in terms of assets reduced leverage during recent years; only firms accounting 13 percent of corporate sector assets (excluding real estate firms) reduced debt in 2020-23.
  - Private firms tended to reduce debt more than SOEs; central SOEs account for about half of the sample in terms of assets and liabilities.

- Post-pandemic scarring on profitability and liquidity
  - Return on assets declined from 2.4 percent in 2017 to 1.9 percent in 2023.
  - Profit margins fell from 18.4 percent to 16.3 percent over 2017–2023.
  - Firms accounting for 7 percent of corporate sector assets (excluding real estate firms) faced profitability risk in 2023.
  - Aggregate cash held by firms: 10 percent of total assets in 2023.
  - Short-term debt: 16 percent of total assets; net accounts payable: 2 percent of total assets.
  - Adjusted cash position (cash net of short-term debt and net accounts payable): negative 8 percent of total assets.
  - Expected cash position (including earnings of 5 percent of total assets and net interest expense of 1 percent of total assets): negative 4 percent of total assets.
  - About two-thirds of firms in terms of assets have a negative adjusted cash position; slightly more than half have a negative expected cash position.
  - Firms classified at profitability risk: share in terms of assets around 8 percent during 2021-23.
  - Firms considered at profitability risk if return on assets is negative; firms considered at viability risk if EBIT less than net interest expense.
  - Share of debt of firms with structurally weak debt-servicing capacity (“risky debt”) increased to 2.9 percent in 2023.
  - Vulnerability to shocks:
    - A permanent reduction in earnings by 10 percent would raise the share of risky debt by 6 percentage points.
    - A permanent increase in funding costs by 1 percentage point would raise the share of risky debt by 26 percentage points.
    - Temporary (one-year) income and funding cost shocks would increase risky debt slightly; permanent shocks (more than three years) could structurally weaken debt-servicing capacity.

- Financial condition and vulnerabilities of small businesses
  - In aggregate, small firms display lower leverage and indebtedness, higher profitability, and stronger liquidity (positive net cash position and positive earning cashflows).
  - Distribution shows thick tails: a large portion of both loss-making and highly profitable small businesses.
  - Bank lending to micro and small enterprises has increased sharply in recent years, driven by policy guidance.
  - Financial weakness concentration among small firms:
    - Share of small firms with negative profits increased to 24 percent in 2023 (compared to 7 percent for all firms).
    - Share of small firms with structurally weak debt-servicing capacity: 9 percent (compared to 3 percent for all firms).
  - Concerns for banks:
    - Rapid increase in bank lending to small businesses may reflect weakening credit underwriting practices.
    - Large presence of financially weak small firms could result in material credit losses for banks if defaults rise.

- Policy implications and recommended priorities (summary from section D headings and text included)
  - Urgency to proactively address debt of financially weak nonfinancial firms to safeguard macro-financial stability.
  - Policy actions highlighted:
    - Maintain prudential norms.
    - Facilitate debt restructuring where needed.
    - Support market-based mechanisms for credit allocation.
    - Develop contingency planning for managing systemic risk events.
  - Particular attention warranted for property developers given ongoing property sector downturn (real estate sector analyzed separately).

*Italic: Source: References and selected extracts from "ASSESSING VULNERABILITIES OF CHINA’S CORPORATE SECTOR" (PEOPLE’S REPUBLIC OF CHINA), IMF staff text and listed references contained in the provided content unit.*

### 13.      Property developers are highly leveraged. In 2023, their total liabilities to total assets

### 13.      Property developers are highly leveraged. In 2023, their total liabilities to total assets

### Key findings on leverage, liquidity, and solvency
- Total liabilities to total assets for property developers stood at 80 percent in 2023.
- Debt accounted for 39 percent of total liabilities for property developers.
- Other forms of liabilities for property developers:
  - Unearned revenues associated with presales of yet-to-be-delivered properties: 26 percent of total liabilities.
  - Accounts payables to contractors and suppliers: 17 percent of total liabilities.
- Property developers have been experiencing aggregate operating losses since 2021.
- Earnings are insufficient to cover interest expenses, contributing to widespread defaults in recent years.
- If developers facing liquidity shortfalls are forced to liquidate assets at prices below book values, many could see common equity become negative.
- In the sample, about 72 percent of property developers (in terms of assets) have defaulted on their bonds (mostly, offshore bonds).
- Bank lending to property developers amounted to 3 percent of banking sector assets at end-2023.
- Offshore bond defaults do not appear to have had a direct impact on the onshore financial system so far.
- Lack of loss recognition, partly driven by regulatory forbearance for banks, has temporarily limited stress in China’s financial system; losses have so far been concentrated among certain investors such as investors in trust products.

### Quantitative indicators and sample coverage
- Real estate sample:
  - Around 470 entities.
  - Aggregated total assets: 36 trillion RMB (29 percent of GDP).
  - Aggregated total debt: 11 trillion RMB (9 percent of GDP).
- Nonfinancial firms excluding real estate:
  - Nearly 20,000 entities.
  - Aggregated total assets: 333 trillion RMB (264 percent of GDP).
  - Aggregated total debt: 128 trillion RMB (101 percent of GDP).
- Small firms (NEEQ and regional exchanges; total assets < 1 billion RMB):
  - About 6,400 entities.
  - Aggregated total assets: about 1,400 billion RMB.
  - Aggregated total debt: about 250 billion RMB.
- Aggregate analysis coverage:
  - More than 24,000 corporate entities.
  - Total assets of 398 percent of GDP and total debt of 151 percent of GDP.
  - Non-debt liabilities equal 105 percent of GDP.

### Risks and transmission channels
- Financial vulnerabilities of property developers have risen sharply: sustained operating losses, significant exposure to liquidity and solvency risks, and weak debt-servicing capacity.
- Solvency risk is material and could increase further amid falling property prices.
- Contagion risk: overall bank losses could become much larger if the unfinished housing problem remains unaddressed and/or financial stress spills over to business partners of property developers.
- Asset quality risks related to small firms: rapid increase in bank lending to small businesses, partly driven by policy guidance, may weaken credit underwriting practices and increase banks’ asset quality risk.
- A non-negligible portion of mortgages are backed by unfinished housing, potentially amplifying transmission.

### Policy recommendations and remedial actions
- Maintain prudential norms:
  - Remove regulatory forbearance introduced during the pandemic and in response to the property sector downturn.
  - Require lenders to properly recognize credit losses and appropriately provision.
  - Strictly enforce prudential requirements, supported by credible recovery planning to ensure financial institutions’ adequate capitalization to manage nonperforming assets and continue credit intermediation.
- Facilitate debt restructuring:
  - Accelerate restructuring and/or orderly exits for property developers while ensuring delivery of pre-sold housing.
  - Modernize the corporate bankruptcy framework to improve reorganization effectiveness and enhance use of hybrid restructuring techniques.
  - Establish a special insolvency process for small businesses.
  - Introduce a nationwide legal framework for personal insolvency to facilitate orderly resolution of financial difficulty of sole proprietors.
- Support market-based mechanisms for credit allocation:
  - Phase out policy guidance on overall credit growth targets, preferential access to credit for certain industries, and implicit government support, as they result in inefficient resource allocation, mispricing of credit risk, and inadequate risk management.
- Develop contingency planning:
  - Create contingency planning to mitigate financial contagion and support functioning of core funding markets to minimize disruptions to economic activity.
  - Develop market-wide liquidity support schemes to ease financial stress in core funding markets, including corporate bond and repo markets, which could be stressed when markets reassess credit risk of credit bonds.

*Source: IMF staff analysis in the provided content unit.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2024/english/1chnea2024004-print-pdf.pdf_