## wpiea2026185-source-pdf ## Source details **Canonical URL:** [wpiea2026185-source-pdf](https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026185-source-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2026/english/wpiea2026185-source-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2026/english/wpiea2026185-source-pdf.pdf.json) --- ### Energy transition scenarios and key simulation findings - Three scenario sets examined: - Baseline shift toward renewable electricity generation; ongoing shift important but insufficient alone to achieve net-zero after 2030. - Higher electricity demand driven by AI-related data centers: electricity price increases can be avoided if additional capacity is supplied by renewable energy instead of coal. - Alternative pathways to achieve China’s official net-zero emissions target by 2060 through higher policy ambition. - Policy implication: - Implementing cost-effective, economy-wide policies – such as a comprehensive quantity-based emissions trading system (QB-ETS) – would make it possible to achieve the net-zero target at a relatively low GDP cost. - Carbon price signals: - Required implicit carbon price ranges from USD 50 to USD 275 per tonne of CO2 by 2040, depending on the pathway chosen to achieve net-zero emissions by 2060. - Multi-model literature reference: carbon prices as high as 1,134 yuan (USD 160) per tonne of CO2 by 2050 still insufficient to achieve 2060 carbon neutrality in some models. ### Stranded assets: projections, drivers, and financial exposure - Projected stranded coal capacity (GW): - Increase from 3 to 128 GW in 2030 to 61 to 236 GW by 2040, depending on electricity demand and battery integration. - Scenario-specific installed coal capacity (GW) and percent changes (2030, 2035, 2040; % change 2040 vs 2030): - LowGrCoal: 1,264; 1,142; 814; "-35.6%" - LowGrBattery: 1,011; 758; 430; "-57.5%" - HighGrCoal: 1,336; 1,226; 894; "-33.1%" - HighGrBattery: 1,068; 814; 473; "-55.7%" - AICoal: 1,354; 1,255; 926; "-31.6%" - AIBattery: 1,068; 814; 473; "-55.7%" - Stranded capacity (GW) and share of committed capacity ("1,397 GW" as of June 2025): - LowGrCoal: 25 (2030), 43 (2035), 90 (2040); share = "2%", "3%", "6%". - LowGrBattery: 128 (2030), 179 (2035), 236 (2040); share = "9%", "13%", "17%". - HighGrCoal: 6 (2030), 20 (2035), 68 (2040); share = "0%", "1%", "5%". - HighGrBattery: 103 (2030), 155 (2035), 216 (2040); share = "7%", "11%", "15%". - AI_Coal: 31 (2030), 36 (2035), 10 (2040); share = "1%", "1%", "4%". - AI_Battery: 103 (2030), 155 (2035), 216 (2040); share = "7%", "11%", "15%". - Key assumptions and sensitivities: - Committed capacity (operating + under-construction) = "1,397 GW" as of June 2025. - Capacity factor for coal-fired plants assumed to be "0.55". - Batteries reduce need for coal backup capacity by "50 percent in 2030", "75 percent in 2035", and fully replace it by 2040. - Baseline includes all coal power plants operational or under construction as of "June 2025". - Average coal plant lifetime assumed to be "40 years" (sensitivity analyses use "30- and 50-year" lifespans). - Stranded capacity per plant = installed capacity × share of expected operating lifetime lost; retirement order: oldest units first. - Halting new coal construction could cut stranded capacities in 2040 by two-thirds in the HighGrCoal scenario: from "68 GW" to "22 GW". - As of June 2025, over "200 GW" of new coal capacity is under construction, with an additional "100 GW" permitted. - With extensive battery storage, coal capacity projected to drop to "473 GW" by 2040 (HighGrBattery), and to "430 GW" if economic growth slows (LowGrBattery), increasing stranded capacity to "216 GW" in HighGrBattery and AIBattery, and up to "236 GW" in LowGrBattery ("17 percent" of committed capacity). - Average plant age: national average "15 years"; Xinjiang average "10 years". - Sensitivity to lifetimes: in HighGrCoal, no investment losses at "30-year" lifetime, but stranded capacity could reach "16 GW" in 2030 and "147 GW" in 2040 if a "50-year" lifetime is assumed. - Financial exposure: - Total bank exposure to coal power is less than "1 percent" of total loans. - Regional exposures can be much higher (notably Ningxia, Xinjiang, Inner Mongolia). - Overall financial impact manageable at national level; severe consequences possible for specific regions with higher local exposures. ### Managing large-scale coal replacement and system flexibility - Scale of the challenge: - China has more than twice as much coal capacity than the entire OECD. - Capacity of coal power plants constructed in China between 2005 and 2025 is "24 979 GW" (source text assertion). - Social and labor dynamics: - Coal sector employment declined from a peak of "5.3 million" in 2014 to "2.5 million" in 2024. - Renewable energy projects estimated to replace "25 percent" of jobs lost in coal industry; many replacement jobs require long-distance relocation. - In employment terms, half of the coal phaseout has already been achieved due to automation. - Portfolio of technological and system responses (IEA framework, stage three of six): - Grid expansion - Pumped hydro storage - Battery systems - Green hydrogen - Greater flexibility on both demand and supply sides of the electricity system - Conversion and reuse options for coal plant infrastructure: - Retrofit for biomass co-firing combined with CCS. - Conversion to use only biomass or green hydrogen (limitations: biomass availability constrained; green hydrogen more expensive than natural gas). - Reuse as synchronous condensers or batteries. ### Literature synthesis — selected quantitative highlights - Storage growth: various storage technologies expected to grow at rates of about "15 percent" per year, with lithium-ion batteries dominant by 2035 (Yu et al., 2024). - AI impacts: AI can widen the income gap and increase non-renewable energy consumption (Chen et al., 2025). - Stranded-asset estimates in literature: - Continued coal construction could increase stranded coal asset values from USD "55 billion" to between three and seven times that amount (Zhang et al., 2023). - "6 percent" of total installed coal capacity likely to be stranded if China reaches carbon neutrality by 2060 (Wu et al., 2024a). - Economy-wide policy analyses: - An economy-wide ETS consistent with China’s NDC reduces real GDP by "2.1 percent" relative to a no-policy baseline. - An ETS limited to eight energy-intensive sectors results in a GDP loss of "10.5 percent". - Expanding coverage to include nine additional sectors (raising emissions coverage to "76 percent") lowers the GDP impact to "3.3 percent". ### Model description, database, and scenario design - Model: IMF-ENV, a global recursive dynamic CGE model used by the IMF Research Department; full documentation in Chateau et al. (2025). - Core model features: - Neo-classical framework optimizing consumption and production decisions. - Two capital vintages: new capital (reallocatable) and old installed capital (putty-clay). - Labor supply determined by working-age population, participation, and long-term unemployment; labor adjusts endogenously to real wages. - Production: nested CES functions; household demand non-homothetic; trade uses Armington specification. - Model runs until 2040. - Database and disaggregation: - Central input: GTAP-Circular Economy (CE) database (version 11) with base year 2017. - Database: input–output tables for 141 countries and 19 aggregate regions; project uses G20 aggregation with 25 regions, 39 activities and 31 commodities. - Energy detail: eight electricity generation technologies (coal, natural gas, oil (diesel), hydro, nuclear, solar, wind and others) plus electricity transmission and distribution. - Greenhouse gases included: CO2, CH4, N2O and fluorinated gases. - Scenario comparison: - Business-as-usual (baseline) with frozen trade, energy and climate policies versus policy counterfactuals introducing new/alternate policies. - Impacts estimated on GDP, sectoral production and employment, bilateral trade, electricity generation mix, energy demand, and GHG emissions. ### Baseline scenario calibration and electricity projections - Two baseline growth paths to 2040: - Low-growth baseline: - Average growth rate "3.8 percent" from 2025 to 2030 and "2.8 percent" from 2031 to 2040. - By 2040, growth rates close to "2 percent". - Total investment projected to remain steady at approximately "38 percent" of GDP. - High-growth baseline: - Projects potential average annual growth at around "4.3 percent" between 2025 and 2040. - By 2040, growth rates close to "3 percent". - Investment shares gradually decreasing to around "30 percent" of GDP by 2040. - Electricity supply projections: - Baseline supply sourced from NGFS Phase V GCAM model (NGFS, 2024). - Counterfactual supply from China Energy Transformation Outlook Report (CETO) (ERI, 2024). - Solar generation costs decline by "3 percent annually". - Wind generation costs decline by "2 percent annually". - Under the baseline (high-growth) scenario, total electricity supply reaches a level "73 percent higher in 2040 than in 2023". - Counterfactuals foresee total electricity generation reaching "80 percent above 2023 levels by 2040" (does not include AI-related higher demand). ### Counterfactuals, electrification assumptions, and VRE backup technology - Counterfactuals use CETO projections for capacity, generation mix, electrification rates, and infrastructure investments as a share of GDP. - Electrification assumptions: - All household vehicles electric by "2035". - Electricity accounts for approximately "97 percent" of total household energy demand by "2040". - By "2040" electricity represents "50 percent" of intermediate energy demand in manufacturing and increases commercial transportation share from "around 14 percent" to "28 percent". - Two primary VRE backup assumptions by 2040: - VRE backed by coal: - VRE = "57 percent" of total generation. - "422 GW" of installed coal capacity used for backup. - Additional yearly investment needs average "2.3 percent of GDP". - VRE backed by batteries: - VRE = "63 percent" of total generation. - "0 GW" of coal capacity for backup. - Average annual investment requirements fall to "2.1 percent of GDP". - Batteries increase usable solar generation by six percentage points. - Scenario matrix (key attributes): - Low GDP growth: From "5% in 2025 to 2% in 2040" (LowGrCoal, LowGrBattery). - High GDP growth: From "5% in 2025 to 3% in 2040" (HighGrCoal, HighGrBattery). - AI data centers (high growth): "3.5% increase in electricity demand" (AICoal, AIBattery). - Net Zero (high growth): NZNGFS, NZ-peak2025, NZ-peak2027. ### AI-driven electricity demand scenarios and effects - AI data center demand growth: - Based on Bogmans et al. (2026): data center electricity demand grows about "10 percent annually", reaching roughly "3.5 percent of total electricity supply by 2040". - Two AI scenarios (high GDP growth): - AICoal: "3.5 percent" increase provided fully by coal generation. - AIBattery: "3.5 percent" increase provided fully by solar combined with battery storage. - Electricity price effects (comparisons to non-AI counterparts): - When coal is backup: - Electricity price increase about "one percentage point higher" with AI: "5.5 percent" vs. "4.6 percent". - By 2040, price reduction lower with AI: "–3.8 percent" instead of "–4.7 percent". - When batteries are backup: - In 2030, electricity prices increase by "0.6 percentage points" with AI: "2.7 percent" vs. "2.1 percent". - By 2040, price reductions larger under higher AI demand: "–8.4 percent" versus "–7.6 percent". - Net effects: - Medium- and long-term reductions in electricity prices from the green transition are slightly dampened with AI demand; overall effects on GDP and energy security remain positive. ### Net-zero emissions pathway scenarios and policy costs - Net-zero scenarios: - NZ-NGFS: NGFS Phase V net-zero pathway, emissions peak in "2025" and decline rapidly. - NZ-peak2025: emissions peak in "2025" then decline linearly until "2060". - NZ-peak2027: emissions peak in "2027" then decline linearly until "2060". - To meet net-zero by 2060, model introduces QB-ETS for non-electricity emissions; model endogenously determines implicit carbon price. - ETS outcomes under NGFS net-zero pathway: - Implicit ETS carbon price must rise to "about USD 275 per tonne of CO2 by 2040". - GDP costs exceed "4 percent of real GDP by 2040" relative to the baseline under NGFS. - Chinese economy projected to grow at an average rate of "around 4 percent" over this period. - Alternative net-zero timing outcomes: - NZ-peak2025: implicit ETS carbon price by 2040 is "USD 56"; GDP impact well below "1 percent of GDP" in 2040 relative to baseline. - NZ-peak2027: implicit ETS carbon price by 2040 is "USD 78"; GDP impact well below "1 percent of GDP" in 2040 relative to baseline. - Summary findings: - Batteries vs coal backup: batteries provide greater flexibility and lead to greater long-term reductions in electricity prices. - Energy security improves with higher reliance on renewables. - Green transition with increased renewable generation and electrification can substantially contribute toward China’s 2060 net-zero target at relatively low GDP costs. - Greater reliance on batteries increases the size of stranded assets within the coal power sector. ### Macroeconomic outcomes: energy expenditures, energy security, and long-run transition outcomes - Energy-related expenditure as a share of GDP: - Falls from "11.4 percent in 2024" to "around 8.0 percent" under the high growth scenario by 2040. - Falls to "8.9 percent" under the low growth scenario by 2040. - Drivers: lower electricity prices; higher share of electricity in total energy demand; reduced demand for imported fossil fuels. - Model-simulated changes (IMF-ENV): - Energy import dependence declines by "about 2.2 to 2.5 percent" in the coal backup case by 2040. - Additional reduction of "around 0.1 percentage point" when battery storage is included relative to baseline. - Energy-related expenditure share falls by "about 1.7 to 1.8 percent" with coal backup, with a further decline of "about 0.2 percentage point" in the battery storage scenario. - Reductions larger under the high growth scenario than under the low growth scenario. - GHG emissions reductions: - When coal provides backup: total GHG emissions fall by "about 12 percent" by 2030 and "20–24 percent" by 2040. - When pathways rely on battery storage: reductions of "around 15–16 percent" by 2030 and "24–27 percent" by 2040. - Energy-price trajectory: medium-term cuts aligned with pathways peaking between "2025 and 2027", but insufficient to meet Net‑Zero targets by 2040 without additional policies. ### Policy implications and transition management - Key policy areas: - Implement cost-effective, economy-wide policies (e.g., QB-ETS) to close the emissions gap toward 2060 net-zero. - Phase out new coal construction to substantially reduce future stranded capacity. - Support workers and regions affected by coal phaseout via retraining, relocation assistance, severance/early-retirement mechanisms, and directing new investment to coal regions (e.g., repurposing mines and grid connectors). - Ensure grid stability and system flexibility through investments in grid expansion, pumped hydro, battery systems, green hydrogen, and demand-side flexibility. - Consider targeted conversions (biomass co-firing + CCS, conversion to biomass or green hydrogen) and reuse options (synchronous condensers, batteries) where feasible. - Regional and distributional considerations: - Top ten provinces account for about "two-thirds" of total stranded capacity across scenarios; Inner Mongolia, Shanxi, Ningxia, Xinjiang highlighted as high-risk regions. - Financial stability: national banking exposure to coal power < "1 percent" of loans, but regional concentrations can be much higher. *Source: wpiea2026185-source-pdf - 2.0 percentage points from the baseline level of around 8.5 percent in 2040.* ### 2.0 percentage points from the baseline level of around 8.5 percent in 2040. ### wpiea2026185-source-pdf - 2.0 percentage points from the baseline level of around 8.5 percent in 2040. ### Energy transition scenarios and key simulation findings - Three sets of energy transition scenarios examined: - Baseline shift toward renewable electricity generation; ongoing shift important but insufficient alone to achieve net-zero after 2030. - Higher electricity demand driven by AI-related data centers: electricity price increases can be avoided if additional capacity is supplied by renewable energy instead of coal. - Alternative pathways to achieve China’s official net-zero emissions target by 2060 through higher policy ambition. - Policy implication from scenarios: - Implementing cost-effective, economy-wide policies – such as a comprehensive quantity-based emissions trading system – would make it possible to achieve the net-zero target at a relatively low GDP cost. - Carbon price signals cited: - Required implicit carbon price ranges from USD 50 to USD 275 per tonne of CO2 by 2040, depending on the pathway chosen to achieve net-zero emissions by 2060. - Multi-model literature reference: carbon prices as high as 1,134 yuan (USD 160) per tonne of CO2 by 2050 still insufficient to achieve 2060 carbon neutrality in some models. ### Stranded assets: projections and drivers - Projected stranded coal capacity: - Increase from 3 to 128 GW in 2030 to 61 to 236 GW by 2040, depending on future electricity demand and the degree of battery integration. - Role of investment decisions: - Halting new coal construction could reduce stranded coal capacity in 2040 by as much as two-thirds. - Interactions with storage and renewables: - Large-scale battery adoption as backup may increase stranded-asset risk for coal plants. - Rapid declines in renewable and storage costs could help offset financial challenges from stranded assets. - Financial exposure: - Overall financial impact of stranded coal power assets is manageable at the national level given the relatively low exposure of China’s banking sector. - Consequences could be severe for specific regions with higher local exposures to coal power. ### Managing large-scale coal replacement and system flexibility - Scale of challenge: - China has more than twice as much coal capacity than the entire OECD; phasing out coal is a monumental task. - Social and labor dynamics: - Coal sector employment has already declined by more than half since its peak in 2014. - Several countries have transitioned without major social disruption; China has made considerable progress. - Portfolio of technological and system responses: - China is preparing to enter stage three (of six) of the IEA framework for integrating solar and wind by investing in: - Grid expansion - Pumped hydro storage - Battery systems - Green hydrogen - Greater flexibility on both demand and supply sides of the electricity system ### Literature synthesis (selected quantitative highlights) - Electrification, renewables, and storage required for carbon neutrality (Zhang and Chen, 2022). - Complementary infrastructure needed: ultra-high-voltage transmission, energy storage, power–load flexibility (Wang et al., 2023b). - Transition outcomes: cleaner energy reduces electricity costs for households, optimizes industrial structures, decreases fossil fuel usage (Gao et al., 2024). - Storage growth: various storage technologies expected to grow at rates of about 15 percent per year, with lithium-ion batteries dominant by 2035 (Yu et al., 2024). - AI impacts: AI can widen the income gap and increase non-renewable energy consumption (Chen et al., 2025). - Stranded-asset estimates in literature: - Continued coal construction could increase stranded coal asset values from USD 55 billion to between three and seven times that amount (Zhang et al., 2023). - 6 percent of total installed coal capacity likely to be stranded if China reaches carbon neutrality by 2060 (Wu et al., 2024a). - Economy-wide policy analyses: - An economy-wide ETS consistent with China’s NDC reduces real GDP by 2.1 percent relative to a no-policy baseline. - An ETS limited to eight energy-intensive sectors results in a GDP loss of 10.5 percent. - Expanding coverage to include nine additional sectors (raising emissions coverage to 76 percent) lowers the GDP impact to 3.3 percent. ### Model description and scenario design - Model: IMF-ENV, a global recursive dynamic CGE model used by the IMF Research Department. - Full model documentation referenced in Chateau et al. (2025). - Features detailed description of energy production and consumption; links GHG emissions to specific economic activities. - Allows simulation of carbon pricing, fossil fuel subsidies, energy demand and supply policies, energy efficiency improvements, and new green technologies. - Core model features: - Neo-classical framework optimizing consumption and production decisions. - Two capital vintages: new capital (net investment) reallocatable without friction; old installed capital given and costly to reallocate (putty-clay). - Labor supply determined by working-age population, participation, and long-term unemployment; labor adjusts endogenously to real wages. - Production uses nested CES functions; household demand is non-homothetic; trade uses Armington specification. - Model runs until 2040. - Database and disaggregation: - Central input: GTAP-Circular Economy (CE) database (version 11) with base year 2017. - Database contains input–output tables for 141 countries and 19 aggregate regions; for this project G20 aggregation used with 25 regions, 39 activities and 31 commodities. - Energy detail includes eight electricity generation technologies: coal, natural gas, oil (diesel), hydro, nuclear, solar, wind and others (e.g., geothermal, biomass), plus an electricity transmission and distribution activity. - Greenhouse gases included: CO2, CH4, N2O and fluorinated gases. - Scenario comparison: - Business-as-usual (baseline) with frozen trade, energy and climate policies versus policy counterfactuals introducing new/alternate policies. - Differences used to estimate impacts on GDP, sectoral production and employment, bilateral trade, electricity generation mix, energy demand, and GHG emissions. ### Macroeconomic framework assumptions and baseline growth pathways - Two baseline scenarios for China to 2040 reflecting different reform outcomes: - Low-growth baseline: - Average growth rate at 3.8 percent from 2025 to 2030 and 2.8 percent from 2031 to 2040 (Muir et al., 2024). - By 2040, growth rates close to 2 percent. - Total investment projected to remain steady at approximately 38 percent of GDP. - High-growth baseline: - Based on Cerdeiro et al. (2024), models productivity-boosting structural reforms and a shift toward greater domestic consumption. - Projects potential average annual growth at around 4.3 percent between 2025 and 2040. - By 2040, growth rates close to 3 percent. - Investment shares gradually decreasing to around 30 percent of GDP by 2040. - Fiscal and external balances: - Baseline government and current account balances: absolute levels held constant after 2030 (standard neoclassical macro closure), so their shares relative to GDP decline through 2040. - Rationale: - Declining potential GDP growth reflects diminishing returns to investment, lower productivity growth, and rapid population aging. - High household savings and historically high investment shares in GDP noted as structural features. *Source: wpiea2026185-source-pdf - 2.0 percentage points from the baseline level of around 8.5 percent in 2040.* ### 3.3 Baseline scenario calibration ### 3.3 Baseline scenario calibration ### Macroeconomic calibration - Macroeconomic variables are calibrated based on the growth assumptions detailed in Section 3.2. - Two distinct growth paths are assumed with different assumptions on the shares of investments to GDP, but relatively similar assumptions for the calibration of the government balance and CAB. - Total labor supply evolves in line with projections of working age population consistent with the "Middle of the Road" Shared Socioeconomic Pathway (SSP2) of Dellink et al. (2017). ### Electricity supply projections and composition - Baseline electricity supply projections are sourced from the current policy scenario from the NGFS Phase V GCAM model simulations (NGFS, 2024). - Counterfactual electricity supply projections come from the China Energy Transformation Outlook Report (ERI, 2024) (CETO), which account for expansion of renewable generation and higher electricity demand from greater electrification. - Solar electricity generation costs are assumed to decline by 3 percent annually. - Wind generation costs are assumed to decline by 2 percent annually. - Cost reductions influence electricity prices in proportion to the share of solar and wind in the overall generation mix. - Under the baseline (high-growth) scenario, total electricity supply increases steadily, reaching a level 73 percent higher in 2040 than in 2023. - In the low-growth baseline and counterfactual scenarios (not shown), total electricity supply is approximately 9 percent lower than in the high-growth scenarios; generation-composition shifts in low-growth closely resemble those in the high-growth figures. ### Counterfactual scenario overview - All counterfactual scenarios use CETO projections covering: - electricity capacity and generation (supply), - the electricity mix (proportion of each power source in total generation), - the rate of electrification in transport and industrial sectors, - required infrastructure investments as a share of GDP (grid expansion, renewables, EV charging infrastructure, firm-level electrification investments). - Counterfactual scenarios reflect a major supply-mix shift: sharp reduction in coal generation and large-scale growth in solar and wind capacity. - Counterfactuals foresee a slightly larger increase in total electricity generation than baseline, reaching 80 percent above 2023 levels by 2040 (does not include higher electricity demand from AI-related data centers). - For all counterfactuals, the main (BCNS) scenario in CETO is used as the baseline; it assumes transition toward VRE, strong GDP growth, coal as main backup source, and no additional electricity demand from AI. - CETO’s detailed energy models satisfy technical feasibility constraints and system balancing requirements. ### Electrification assumptions in counterfactuals - All household vehicles are assumed electric by 2035, sharply reducing the share of refined oil in household energy demand. - Combined household reductions in coal and natural gas for heating and cooking lead electricity to account for approximately 97 percent of total household energy demand by 2040. - By 2040 electricity represents 50 percent of intermediate energy demand in manufacturing and doubles in commercial transportation from the current value of around 14 percent to 28 percent. ### Policy environment in counterfactuals - Energy transition driven by cost reductions in renewable generation from technological change, without additional policies beyond those implicitly included in CETO’s BCNS. - Existing policies incorporated include the current intensity-based ETS in the electricity sector and its planned expansion in 2025 to cover cement, aluminum, iron, and steel industries. - Baseline projections for total GHG emissions are calibrated using the "current policies" scenario from the NGFS Phase V GCAM model simulations (NGFS, 2024). - In all counterfactual scenarios, GHG emissions are determined endogenously by the model. ### Scenarios varying VRE backup technology and growth - Two dimensions: GDP growth path (low and high) and primary backup technology for VRE (coal or batteries). - Flexible capacity denotes portion of total backup capacity that can be supplied either by coal or battery storage; while relatively small in capacity, it can produce distinct electricity price and macroeconomic impacts. - CETO baseline: VRE backup provided primarily by coal (and to a lesser extent hydropower, nuclear, natural gas). - When VRE is mainly backed up by coal (CETO estimations by 2040): - VRE represents 57 percent of total electricity generation. - 422 GW of installed coal capacity used for backup. - Additional yearly investment needs average 2.3 percent of GDP. - When batteries are the main backup technology: - Assume no coal capacity for backup (0 GW). - Batteries increase usable solar generation by storing excess electricity and releasing it during high demand; using batteries raises the share of solar in total electricity supply by six percentage points, resulting in VRE representing 63 percent of total generation in 2040. - Average annual investment requirements fall to 2.1 percent of GDP. - Battery backup modeling specifics: - Increase renewable generation (reflecting batteries’ dual function). - Deduct investments needed for batteries from total annual investment costs. - Assume no excess coal capacity requirements for VRE backup. - Electricity supply and generation mix remain equal across scenarios while ensuring system balancing and flexibility. ### Scenario matrix (summary of key scenario attributes) - VRE backed up by coal by 2040: - 57% of total generation is VRE. - 422 GW of coal capacity for backup. - 2.3% of GDP invested on green energy. - VRE backed up by batteries by 2040: - 63% of total generation is VRE. - 0 GW of coal capacity for backup. - 2.1% of GDP invested on green energy. - Low GDP growth rates: From 5% in 2025 to 2% in 2040 (scenarios: LowGrCoal, LowGrBattery). - High GDP growth rates: From 5% in 2025 to 3% in 2040 (scenarios: HighGrCoal, HighGrBattery). - AI data centers (with high growth): 3.5% increase in electricity demand (scenarios: AICoal, AIBattery). - Net Zero pathway (with high growth) scenarios: - Based on NGFS pathway (NZNGFS). - Emissions peak in 2025 (NZ-peak2025). - Emissions peak in 2027 (NZ-peak2027). ### Scenarios with increased electricity demand from AI - Based on Bogmans et al. (2026): electricity demand from data center expansion associated with AI in China grows by about 10 percent annually, reaching roughly 3.5 percent of total electricity supply by 2040. - Two AI scenarios added (both with high GDP growth assumptions): - AICoal: electricity demand increases by 3.5 percent provided fully by coal generation. - AIBattery: electricity demand increases by 3.5 percent provided fully by solar combined with battery storage. - AI-related scenarios do not account for additional macroeconomic growth effects from AI itself. - The modeled AI demand shock increases total electricity supply in China rather than sector-specific IT demand. - The shock on China’s projected data-center electricity consumption in 2030 falls within, but toward the lower bound of, the IEA’s forecast range of 260–470 TWh (IEA, 2024b). ### Net-zero emissions pathway scenarios - China pledged to peak emissions before 2030 and achieve net-zero by 2060; 2035 NDC commitments pledge reducing economy-wide net GHG by 7 to 10 percent from peak levels by 2035 (ERI, 2024). - Total GHG emissions recently stabilized around 15 Gigatonnes of CO2-equivalent (GtCO2eq). - Three alternative net-zero timing scenarios considered: - NZ-NGFS: follows NGFS Phase V net-zero pathway where emissions peak in 2025 and then decline rapidly early before moderating. - NZ-peak2025: emissions peak in 2025 then decline linearly until 2060. - NZ-peak2027: emissions peak in 2027 then decline linearly until 2060. - To meet deeper reductions, three additional counterfactual scenarios build on the high-growth baseline with VRE supported by battery storage and simulate a quantity-based emissions trading system (QB-ETS) to curb GHG outside the electricity sector. - Under the QB-ETS, the model endogenously determines the implicit carbon price for non-electricity emissions that aligns with the overall emissions target, accounting for reductions already achieved in the electricity sector via the energy transition. ### Key modeling notes and assumptions - All electricity supply projections are calibrated to align with each of the growth paths employed. - CETO uses ERI-LEAP, ERI-EDO, and CETPA models and uses their Baseline Carbon Neutrality Scenario (BCNS). - Cost reduction assumptions for solar and wind based on Bogdanov et al. (2019). - Nuclear expansion in scenarios reflects only conventional nuclear technologies. - Advanced nuclear technologies are noted as a potential additional low-carbon complement if they reach commercial viability. - Stranded assets analysis examines the assumption of no excess coal capacity requirements for VRE backup (referenced in Section 5). - Where endogenous capital investment differences arise, the difference between model-endogenous investments and CETO-projected higher investment needs (mainly related to grid upgrades) is exogenously imposed in policy simulations. *Source: IMF Staff calculations and scenario descriptions as presented in the source PDF.* ### 11.4 percent in 2024 to around 8.0 percent under the high growth scenario and 8.9 percent under the ### wpiea2026185-source-pdf - 11.4 percent in 2024 to around 8.0 percent under the high growth scenario and 8.9 percent under the ### Energy expenditures, energy security, and long-run transition outcomes - Energy-related expenditure as a share of GDP falls from "11.4 percent in 2024" to: - "around 8.0 percent" under the high growth scenario by 2040. - "8.9 percent" under the low growth scenario by 2040. - Drivers of the decline: lower electricity prices; higher share of electricity in total energy demand due to increased electrification; reduced demand for imported fossil fuels. - Model simulations (IMF‑ENV) show: - Energy import dependence declines by "about 2.2 to 2.5 percent" in the coal backup case by 2040. - Additional reduction of "around 0.1 percentage point" when battery storage is included relative to the baseline. - Energy-related expenditure as a share of GDP falls by "about 1.7 to 1.8 percent" with coal backup, with a further decline of "about 0.2 percentage point" in the battery storage scenario. - Reductions are somewhat larger under the high growth scenario than under the low growth scenario. ### Effects from increased electricity demand from AI - Comparison approach: scenarios with added AI demand (AI_Coal and AI_Battery) vs corresponding scenarios without additional AI demand (HighGrCoal and HighGrBattery). - Electricity price effects when coal is backup: - Electricity price increase is about "one percentage point higher" with AI demand: "5.5 percent" vs. "4.6 percent". - By 2040, price reduction is lower with AI demand: "–3.8 percent" instead of "–4.7 percent". - Electricity price effects when batteries are backup: - In 2030, electricity prices increase by an additional "0.6 percentage points" with AI demand: "2.7 percent" vs. "2.1 percent". - By 2040, electricity price reductions become larger under higher AI demand: "–8.4 percent" versus "–7.6 percent". - Net effects: - Medium- and long-term reductions in electricity prices from the green transition are slightly dampened when increased AI-related electricity demand is included, reducing the positive effects on GDP and energy security indicators, though overall effects remain positive. - GHG emissions reductions: - When coal provides backup: - Total GHG emissions fall by "about 12 percent" by 2030 and "20–24 percent" by 2040. - When pathways rely on battery storage: - Reductions of "around 15–16 percent" by 2030 and "24–27 percent" by 2040. ### Results when targeting net zero emission pathways and carbon price implications - Energy transition scenarios achieve medium-term cuts aligned with pathways peaking between "2025 and 2027", but are insufficient to meet Net‑Zero targets by 2040; a substantial gap remains versus net-zero pathways, particularly versus the NGFS net-zero pathway (NZ_NGFS) which front-loads reductions. - Policy modeled to close the gap: expanded quantity-based ETS in non-electricity activities. - ETS carbon price and GDP costs under NGFS net-zero pathway: - Implicit ETS carbon price must rise to "about USD 275 per tonne of CO2 by 2040". - GDP costs exceed "4 percent of real GDP by 2040" relative to the baseline under NGFS. - Context: Chinese economy projected to grow at an average rate of "around 4 percent" over this period. - Alternative net-zero pathways: - NZ-peak2025: implicit ETS carbon price by 2040 is "USD 56"; GDP impact well below "1 percent of GDP" in 2040 relative to baseline. - NZ-peak2027: implicit ETS carbon price by 2040 is "USD 78"; GDP impact well below "1 percent of GDP" in 2040 relative to baseline. - Summary of simulation findings: - Batteries vs coal backup: batteries provide more flexible grid storage and lead to greater long-term reductions in electricity prices. - Energy security improves as supply relies more on renewable generation. - The green energy transition with increased renewable generation and electrification can substantially contribute toward China’s 2060 net zero target at relatively low GDP costs. - Greater reliance on batteries increases the size of stranded assets within the coal power sector. ### Stranded assets analysis — coal power sector - Coal capacity projections (installed capacity in GW; Table 2): - LowGrCoal: 2030 = "1,264", 2035 = "1,142", 2040 = "814", "% change (2040 vs 2030)" = "-35.6%" - LowGrBattery: 2030 = "1,011", 2035 = "758", 2040 = "430", "% change" = "-57.5%" - HighGrCoal: 2030 = "1,336", 2035 = "1,226", 2040 = "894", "% change" = "-33.1%" - HighGrBattery: 2030 = "1,068", 2035 = "814", 2040 = "473", "% change" = "-55.7%" - AICoal: 2030 = "1,354", 2035 = "1,255", 2040 = "926", "% change" = "-31.6%" - AIBattery: 2030 = "1,068", 2035 = "814", 2040 = "473", "% change" = "-55.7%" - Key assumptions and methodology: - Primary data: energy sector projections reported in the CETO report (ERI, 2024). - Capacity factor for coal-fired plants assumed to be "0.55". - Batteries reduce need for coal backup capacity by "50 percent in 2030", "75 percent in 2035", and fully replace it by 2040. - Baseline includes all coal power plants operational or under construction as of "June 2025". - Average coal plant lifetime assumed to be "40 years" (sensitivity analyses use "30- and 50-year" lifespans). - Stranded capacity calculated by sequentially retiring oldest units first; stranded capacity per plant equals installed capacity times share of expected operating lifetime lost. - Stranded capacity projections (Table 3; Stranded capacity in GW and share of committed capacity in 2025 where committed capacity = "1,397 GW" as of June 2025): - LowGrCoal: stranded capacity = "25" (2030), "43" (2035), "90" (2040); share of committed capacity = "2%" (2030), "3%" (2035), "6%" (2040). - LowGrBattery: stranded capacity = "128" (2030), "179" (2035), "236" (2040); share = "9%" (2030), "13%" (2035), "17%" (2040). - HighGrCoal: stranded capacity = "6" (2030), "20" (2035), "68" (2040); share = "0%" (2030), "1%" (2035), "5%" (2040). - HighGrBattery: stranded capacity = "103" (2030), "155" (2035), "216" (2040); share = "7%" (2030), "11%" (2035), "15%" (2040). - AI_Coal: stranded capacity = "31" (2030), "36" (2035), "10" (2040); share = "1%" (2030), "1%" (2035), "4%" (2040). - AI_Battery: stranded capacity = "103" (2030), "155" (2035), "216" (2040); share = "7%" (2030), "11%" (2035), "15%" (2040). - Context and sensitivities: - Committed capacity (operating + under-construction) = "1,397 GW" as of June 2025. - Stranded assets in 2040 account for about "5 percent" of committed capacity in 2025 in scenarios where coal is main backup; this share roughly triples when batteries are the primary backup. - As of June 2025, over "200 GW" of new coal capacity is under construction, with an additional "100 GW" permitted. - Halting new coal construction could cut stranded capacities in 2040 by two-thirds in the HighGrCoal scenario: from "68 GW" to "22 GW". - Installed capacity projections range from "1,264–1,354 GW" in 2030 and "814–926 GW" in 2040, translating to "3–25 GW" stranded in 2030 and "61–90 GW" in 2040 across scenarios. - With extensive battery storage, coal capacity projected to drop to "473 GW" by 2040 (HighGrBattery), and to "430 GW" if economic growth slows (LowGrBattery), increasing stranded capacity to "216 GW" in HighGrBattery and AIBattery, and up to "236 GW" in LowGrBattery ("17 percent" of currently operating and under-construction coal capacity). - Average plant age: national average "15 years" in operation; Xinjiang average "10 years". - Sensitivity to lifetimes: in HighGrCoal scenario, no investment losses at "30-year" lifetime, but stranded capacity could reach "16 GW" in 2030 and "147 GW" in 2040 if a "50-year" lifetime is assumed. - Geographic concentration: - Top ten provinces account for about "two-thirds" of total stranded capacity across scenarios. - Inner Mongolia and Shanxi noted as high-risk due to transmission-driven coal expansion. - Xinjiang moves from relatively low risk in HighGrCoal to high risk in LowGrBattery due to younger fleet and changes in required installed capacity. - Financial system implications: - Total bank exposure to coal power is less than "1 percent" of total loans. - Regional exposures can be much higher (notably Ningxia, Xinjiang, Inner Mongolia). - Comparison to literature: stranded assets in 2035 amount to less than "0.5 percent of GDP" in China in one study, and sustainable scenarios may be associated with higher overall GDP driven by green technology leadership. ### Managing the energy transition — policy implications - Transition requires complementary policies to phase out coal use while scaling low-carbon generation to avoid economic disruptions and support affected workers. - Two policy focus areas highlighted: - Worker support and job creation during coal phase-out. - Ensuring grid stability while increasing solar and wind power through flexibility options (further detail discussed in subsequent sections of the source text). *Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026185-source-pdf.pdf* ### 6.1 Phasing out coal ### 6.1 Phasing out coal ### Coal capacity and stranded-asset risk - China constructed massive coal capacity between 2005 and 2025, which makes it much more difficult to phase out coal than in the OECD, where most capacity was constructed before 2005. - Historically, coal power plants have been operated for about 50 years (Cui et al., 2019). - Phasing out coal power plants that have operated for a much shorter time is likely to imply financial losses, making them stranded assets. - The entire operating coal fleet in OECD countries in 2025 had a capacity of 431 GW. - The capacity of coal power plants constructed in China between 2005 and 2025 is 24 979 GW. - The OECD has gradually phased out significant capacity in recent years with only minimal additions, while the reverse applies to China. - Phasing out coal can be achieved in the OECD simply by continuing the trends observed since 2010. In China, a complete reversal of the strategy in recent years is required. ### Employment transition and regional impacts - Most employment in the coal sector is in coal mining (Hamilton et al., 2022). - Employment in coal mining decreased from the peak of 5.3 million in 2014 to 2.5 million in 2024 (CEIC, 2025), mainly due to automation (Paredes and Fleming-Muñoz, 2021). - In terms of employment, half of the coal phaseout has already been achieved. - Renewable energy projects are estimated to replace 25 percent of the jobs lost in the coal industry (Niu et al., 2024), but many of these jobs require long-distance relocation (Wu et al., 2024b). - Coal employment is concentrated in a few provinces in the north of the country (Hamilton et al., 2022). - Even in an ambitious decarbonization 2°C scenario, the phaseout of coal in China would take until the 2040s. This gradual decrease means: - A considerable share of workers will transition to retirement. - Another share will find employment in renewable energy. - Policy options to address employment challenges include: - Retraining programs to improve skill match between workers and new industries. - Directing new investment to coal regions, including re-purposing abandoned coal mines into energy storage facilities or using grid connectors of coal power plants to connect renewable energy capacity. - Additional options discussed by Niu et al., 2024. ### International case studies and policy lessons - Four European countries considered as possible inspiration: Czech Republic, Germany, Poland and the UK. - Poland and the Czech Republic are at an earlier stage of the coal phaseout and manage the transition through policies focused on supporting former coal workers. - Poland has overtaken China in reducing reliance on coal (right panel of Figure 16). - Both countries rely strongly on retraining workers and creating new employment. - The Czech Republic focuses efforts on the three most affected regions, including establishment of new industries, education of workers, and improvement of welfare (Ministry of the Environment, 2022). - Poland provides severance payments, encourages early retirement for coal workers, offers subsidies for starting new companies, supports affected areas as special economic zones, and invests public sector resources in former coal regions (Śniegocki et al., 2022). - The UK and Germany focused less on the coal sector and more on supporting the development of new economic activity. - In the UK and Germany the coal industry had been in decline for decades, enabling rapid phaseout once renewable costs fell significantly. - The UK switched off the last remaining 20 GW year by year until reaching zero in 2024. - The UK addressed employment reduction mainly through diversified companies that reassigned coal workers internally. - Germany’s strategy rested on three pillars (Oei et al., 2020): worker retraining and early retirement; investments in modern energy and transportation infrastructure; and improving quality of life in coal mining regions to attract private investment (including education, research facilities, and cultural, recreational, and environmental improvements). ### Policy implications for China’s coal phaseout - China must reverse recent coal capacity additions to meet phaseout objectives observed in OECD peers. - Targeted government measures can mitigate social impacts by: - Supporting retraining programs and improving labor-market matching. - Directing investment to repurpose coal-region assets (e.g., mines to energy storage, grid connectors to renewables). - Using compensation mechanisms such as severance payments and early retirement incentives where appropriate. - Strategic conversions of coal plants could support grid flexibility in a limited number of locations: - Retrofit for biomass co-firing combined with carbon capture and storage (CCS) to reduce emissions (Sun et al., 2025). - Conversion to use only biomass or green hydrogen could be carbon neutral even without CCS (He et al., 2023). - Limitations: biomass availability is constrained (Nie et al., 2022) and green hydrogen is more expensive than natural gas for the foreseeable future (Ueckerdt et al., 2024). - Reuse options for plant infrastructure include synchronous condensers or batteries (Jindal and Shrimali, 2022). *Source: IMF Working Paper chapter 6.1, "Phasing out coal."* ### References ### References ### Major themes in the cited literature - Models, databases, and technical frameworks for global and regional economic–energy analysis (GTAP, GTAP-Power, IMF-ENV, NGFS). - Empirical and modeling studies on China’s power sector decarbonization, coal phaseout, stranded assets, and employment implications. - Technology-specific assessments: renewables (solar, wind, hydro), nuclear and advanced nuclear, carbon capture and storage (CCS), hydrogen (blue and green), energy storage (including pumped storage), and smart grid integration. - Policy and transition case studies and evaluations: carbon pricing (carbon tax, ETS hybrids), just-transition programs, regional adjustment policies, and investment trajectories. - Resource and supply assessments: biomass potential, mining automation/robotics implications, and investment flows in global energy. - Scenario and macroeconomic frameworks: shared socioeconomic pathways, committed emissions from existing infrastructure, and climate scenarios technical documentation. ### Key data sources, models, and reports (selected, preserving titles and years) - Aguiar, A., Chepeliev, M., Corong, E., and van der Mensbrugghe, D. (2022). The global trade analysis project (GTAP) data base: Version 11. Journal of Global Economic Analysis, 7(2):1–37. - Chepeliev, M. (2023). GTAP-Power data base: Version 11. Journal of Global Economic Analysis, 8(2). - Chateau, J., Rojas-Romagosa, H., Thube, S., and van der Mensbrugghe, D. (2025). IMF-ENV: Integrating climate, energy, and trade policies in a general equilibrium framework. IMF Working Paper 2025/077, International Monetary Fund, Washington, DC. - NGFS (2024). NGFS Climate Scenarios Technical Documentation V5. Network for Greening the Financial System. - IEA (2024a). Integrating solar and wind. Report, International Energy Agency. - IEA (2024b). World energy investment 2024. Report, International Energy Agency. - IEA (2025). World energy investment 2025. Report, International Energy Agency. - IRENA (2026). 24/7 renewables: The economics of firm solar and wind. Report, International Renewable Energy Agency, Abu Dhabi. - ERI (2024). China Energy Transformation Outlook (CETO). 2024 Report, Energy Research Institute of the Chinese Academy of Macroeconomic Research. - IMF (2024). People’s Republic of China: Selected Issues. IMF Staff Country Reports 276, International Monetary Fund, Washington, DC. - World Bank (2022). China: Country and Climate Development Report. The World Bank Group, Washington, DC. ### Representative empirical and modeling studies on China (selected citations and focus) - Bogmans, C., Ganpurev, G., Gómez-González, P., Melina, G., Pescatori, A., and Thube, S. (2026). Power hungry: How AI will drive energy demand. Energy Economics, 158:109278. - Fan, J.-L., Li, Z., Huang, X., Li, K., Zhang, X., Lu, X., Wu, J., Hubacek, K., and Shen, B. (2023). A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage. Nature Communications, 14(1):5972. - He, G., Lin, J., Sifuentes, F., Liu, X., Abhyankar, N., and Phadke, A. (2020). Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system. Nature Communications, 11:2486. - Cui, R. Y., Hultman, N., Edwards, M. R., He, L., Sen, A., Surana, K., McJeon, H., Iyer, G., Patel, P., Yu, S., Nace, T., and Shearer, C. (2019). Quantifying operational lifetimes for coal power plants under the Paris goals. Nature Communications, 10(1):4759. - Edwards, M. R., Cui, R., Bindl, M., Hultman, N., Mathur, K., McJeon, H., Iyer, G., Song, J., and Zhao, A. (2022). Quantifying the regional stranded asset risks from new coal plants under 1.5 ◦C. Environmental Research Letters, 17(2):024029. - Wang, Y., Zhang, Y., Wang, K., Liu, J., Wang, T., Yang, Q., and Zhuang, S. (2022). Stranded assets and credit default risk in China’s coal power transition. Report prepared by the Programme of Energy and Climate Economics (PECE), Renmin University of China, Beijing, China. - Wu, H., Liu, J., Hu, X., He, G., Zhou, Y., Wang, X., Liu, Y., Ma, J., and Tao, S. (2024b). Fewer than 15% of coal power plant workers in China can easily shift to green jobs by 2060. One Earth, 7(11):1994–2007. - Wang, Y., Zhang, Y., Wang, K., Liu, J., and Pan, X. (2026). Unit-level stranded asset of coal power under low-carbon transition pathways toward China’s carbon neutrality. Energy Policy, 211:115132. ### Concordance tables contained in the appendix (Tables 4–6) - Table 4: Concordance for commodities (i) between IMF-ENV and the GTAP database - Lists commodity group numbers 1 to 31 and their IMF-ENV labels with corresponding GTAP items. Examples: - 1 All Crops (cro) — Paddy Rice (pdr), Wheat (wht), Cereal grains nec (gro), Vegetables, fruits, nuts (vf), Oil Seeds (osd), Sugar cane, sugar beet (cb), Plant-based fibers (pfb), Crops nec (ocr) - 5 Coal extraction (coa) — Coal (coa) - 24 Electricity (ELY) — Coal power baseload (CoalBL), Coal-based CCS (colccs), Oil power baseload (OilBL), Oil power peakload (OilP), Gas power baseload (GasBL), Gas power peakload (GasP), Gas-based CCS (gasccs), Nuclear power (NuclearBL), Advanced nuclear (advnuc), Hydro power baseload (HydroBL), Hydro power peakload (HydroP), Wind power (WindBL), Solar power (SolarP), Other baseload includes biofuels, waste, geothermal, and tidal technologies (OtherBL), Electricity transmission and distribution (TnD) - Notes: nec = not elsewhere classified. Commodities 1 to 4 correspond to agriculture, 5 to 8 to mining, 9 to 23 to manufacturing, and 24 to 31 to services. - Table 5: Concordance for activities (a) between IMF-ENV and the GTAP-CE database (version 11c) - Lists activity group numbers 1 to 39 and their IMF-ENV labels with corresponding GTAP-CE items. Examples: - 1 All Crops (cro) — Paddy Rice (pdr), Wheat (wht), Cereal grains nec (gro), Vegetables, fruits, nuts (vf), Oil Seeds (osd), Sugar cane, sugar beet (cb), Plant-based fibers (pfb), Crops nec (ocr) - 24 Coal powered electricity (clp) — Coal power baseload (CoalBL), Coal-based CCS (colccs) - 26 Gas Powered electricity (gsp) — Gas power baseload (GasBL), Gas power peakload (GasP), Gas-based CCS (gasccs) - 32 Electricity transmission and distribution (etd) — Electricity transmission and distribution (TnD) - Notes: nec = not elsewhere classified. Activities 1 to 4 correspond to agriculture, 5 to 8 to mining, 9 to 23 to manufacturing, and 24 to 39 to services. - Table 6: Regional concordance between IMF-ENV and the GTAP database - Lists region numbers 1 to 25 with IMF-ENV region labels and corresponding GTAP country/group codes. Examples: - 1 Argentina (Argentina) — Argentina (ARG) - 5 China (CHN) — China (CHN) - 12 Korea (Korea) — Republic of Korea (KOR) - 19 United States (USA) — United States of America (USA) - 20 Rest of EU & EFTA (REU) — Austria (AUT), Belgium (BEL), Cyprus (CYP), Czech Republic (CZE), Denmark (DNK), Estonia (EST), Finland (FIN), Greece (GRC), Hungary (HUN), Ireland (IRL), Latvia (LVA), Lithuania (LTU), Luxembourg (LUX), Malta (MLT), Netherlands (NLD), Poland (POL), Portugal (PRT), Slovakia (SVK), Slovenia (SVN), Spain (ESP), Sweden (SWE), Switzerland (CHE), Norway (NOR), Rest of EFTA (XEF), Bulgaria (BGR), Croatia (HRV), Romania (ROU) - Other aggregated regions and their constituent countries are listed verbatim (e.g., Other Middle East countries (ROP), Other Asian countries & New Zealand (ODA), Other African countries (OAF), Other East European and Eurasian countries (OEA), Other Latin American countries (OLA)). *Macroeconomic Impacts of China’s Energy Transition — Working Paper No. WP/2026/185 (References)* --- _Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026185-source-pdf.pdf_