## 2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis ## Source details **Canonical URL:** [2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis](https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023184-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2023/english/wpiea2023184-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2023/english/wpiea2023184-print-pdf.pdf.json) --- ### 2.1 Stylized Facts for a Mid-transition Period - Global decarbonization targets and required emission reductions: - For 1.5°C: emissions must peak before 2025 and decline by 43 percent before 2030 compared to 2019 levels. - For 2°C: emissions must peak before 2025, be reduced by 21 percent before 2030 and 64 percent before 2050 compared to 2019 levels. - These objectives imply net zero emissions reached around 2050 (1.5°C) and 2070 (2°C) respectively. - Current policy commitments for 2030 largely fail to credibly align with a neutrality horizon (Black et al. 2022). - Structural and technological transformation drivers: - Switch to renewable energy and electrical vehicles. - Technological change already committed by past climate policies (Mercure et al., 2021). - Investment and market facts: - Some estimates put the clean energy shortfall at nearly US$3 trillion per year (IEA, 2022a). - Investment in unabated fossil generation remains high, on a par with its 2016 level (IEA, 2023; IMF, forthcoming a). - Diffusion, tipping points, and stranded-asset dynamics: - Low-carbon deployment is self-reinforcing through learning-by-doing and increasing returns (Way et al., 2022; Mercure 2012; Mercure et al., 2014; Arthur, 1994; Unruh, 2000). - Tipping points toward irreversible diffusion could be near or past for solar energy and electric vehicles (Sharpe and Lenton, 2021; Nijsse et al., 2023; Lam and Mercure, 2022). - If transition gains momentum, fossil assets could become stranded; excessive anticipation of stranding could produce price spikes (Pfeiffer et al., 2018; Mercure et al., 2018a, 2021; Semieniuk et al., 2022; van der Ploeg and Rezai, 2020). - Example: OPEC revised its 2045 global oil demand projection upward from 100.6 to 110 million barrel per day in an interval of only 9 months (OPEC, 2022; Reuters, 2023). - Trade and resource structure: - Declining demand for fossil fuels could reduce trade in fossil commodities without comparable replacement by trade in renewable energy; international trade in green hydrogen will unlikely match today’s volumes of trade in fossil fuels (IRENA, 2022). - Projected growth in trade of low-carbon technology/capital goods and critical minerals (Boer et al., 2021). - Geological reserves likely sufficient through 2050 (Wang et al., 2023), but demand growth for many critical materials currently far exceeds rate of supply expansion (Miller et al., 2023). - Political and policy context: - Policy actions with cross-border implications include the EU’s Carbon Border Adjustment Mechanism, the U.S. Inflation Reduction Act, and China’s decision to stop financing coal power plants under the Belt and Road Initiative (Magacho et al., 2023a). - Definition of “mid-transition”: - Period when low-carbon and fossil-based socio-technical regimes are both undergoing rapid transformation, co-exist on a large scale, and operate in a highly contested space (Grubert and Hastings-Simon, 2022). - This period creates new or exacerbates existing instabilities and volatility of global energy markets. - Literature gaps and paper contributions: - Existing literature often assumes globally coordinated, cost-effective scenarios and understates structural, cross-border transformation. - This paper: (i) draws on stylized facts to show global decarbonization will very likely change the structure of international trade and capital flows; (ii) proposes an analytical framework based on a system dynamics approach to analyze cross-border effects; (iii) assesses a subset of likely impacts on output and trade using the E3ME-FTT global macroeconomic model. ### 2.2 Emerging Cross-Border Risks of the Mid-Transition Period - Key channels of cross-border risk: - Trade channels: shifts in trade of fuels, technologies, and critical materials affect exporters and importers differently, altering trade balances and exchange rates. - Financial channels: changes in asset valuations (including stranded assets) and cross-border capital flows can create sovereign and financial system vulnerabilities. - Labor and employment channels: reallocation of employment across sectors and geographies with fiscal and social implications. - Policy heterogeneity: differences in stringency and pace of climate policies across countries affect cross-border outcomes and distribution of benefits/costs (Dubash, 2021; IMF, 2022). - Distributional implications across major economies (overview results): - China, India and Japan are likely to benefit the most from the transition relative to a hypothetical baseline of baseline GDP growth. - Russia, Saudi Arabia, the U.S. could be negatively affected relative to that hypothetical baseline. - Scope and caveats: - Analysis highlights cross-border risks without minimizing transition opportunities (economic growth from low-carbon infrastructure, job creation, innovation, environmental and health co-benefits; Bhattacharya et al., 2021). - Counterfactual “no (or failed) transition” would imply far greater cross-border risks from Earth system destabilization. - Further structure: - Section 3: stylized facts and taxonomy of country-level cross-border risks. - Section 4: quantified analysis of potential cross-border impacts using E3ME-FTT. - Section 5: exploration of international macroeconomic and financial spillovers. - Section 6: summary and reflections on future research. ### Overview: additional stylized facts (summarized) - Parallel coexistence of fossil and low-carbon technologies raises uncertainty and instability; decision-making occurs “under dynamic and uncertain conditions.” - Market and policy interactions can amplify or mitigate geoeconomic fragmentation; uncoordinated industrial policies (China’s Five-Year Plans, U.S. Inflation Reduction Act, EU’s “Fit for 55”, REPowerEU, EU Green Deal Industrial Plan, Net Zero Industry Act) are salient examples. - Resource and supply risks: - Recent increases in costs of critical materials could halt or reverse cost declines in some low-carbon technologies (IEA 2021a). - Material substitution faces technological obstacles (e.g., aluminum substituting for cobalt and lithium in batteries remains a major technological challenge). - Geopolitical risks include potential quantitative export restrictions on critical materials. ### Country archetypes, trade mapping, and archetype interactions - Five country archetypes characterize cross-border feedbacks; many countries combine characteristics of two or more archetypes. - Archetype mappings and G20 highlights: - High-carbon locked-in countries (high net fossil exports, low transition outlook): Saudi Arabia, Russia, Australia, Canada, and to a lesser extent Indonesia. - Fossil importer countries: the EU, Japan, Korea, India, and China. - Critical mineral exporter countries (low fossil net exports, critical mineral net exports, low green transition outlook): South Africa, Australia, Brazil. - Critical mineral importer and cleantech exporter countries (low brown lock-in, critical minerals net imports, high green transition outlook): China, Korea, Japan, the EU; India possibly included with lower green transition outlook. - U.S.: between clusters, with relatively high green transition outlook, small dependency on critical minerals, relatively low dependency on fossil imports, but a relatively large fossil producer and moderate critical mineral producer. - Geoeconomic fragmentation implications: - Fragmentation could shorten value chains and incentivize reshoring, reducing some cross-border risks while amplifying others. - Historical context: world goods exports roughly doubled from 10 percent to 20 percent of world GDP from 1990 to 2008 (Shin, 2023). - Political, fiscal, conflict and inflation regime risks: - Scarce transition resources can be linked to conflict, especially in fragile states. - Fossil-exporting countries may face fiscal pressures and social unrest if current account deteriorates and domestic fossil price regimes must be reformed. - Possibility of a high-inflation regime that reduces certainty and deters productive and FDI; higher average temperatures exert persistent inflationary pressure (Kotz et al., 2023). - Developmental dilemmas: - Balance-of-payments constraints may force a dilemma between rapid decarbonization and raising income per capita (Valdecantos, 2023). - Sufficiency strategies (IPCC, 2022) could emerge to improve energy/material security, trade balances, and strategic autonomy. *Source: IMF Working Paper — 2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis (excerpt).* ### 2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis _______________________ 5 ### 2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis ### 2.1 Stylized Facts for a Mid-transition Period - Global decarbonization targets and required emission reductions: - For 1.5°C: emissions must peak before 2025 and decline by 43 percent before 2030 compared to 2019 levels. - For 2°C: emissions must peak before 2025, be reduced by 21 percent before 2030 and 64 percent before 2050 compared to 2019 levels. - These objectives imply net zero emissions reached around 2050 (1.5°C) and 2070 (2°C) respectively. - Current policy commitments for 2030 largely fail to credibly align with a neutrality horizon (Black et al. 2022). - The global economy is undergoing a large-scale transformation in technological, trade, and financial domains driven by: - A switch to renewable energy and electrical vehicles. - Technological change already committed by past climate policies (Mercure et al., 2021). - Cross-border implications are underexplored relative to domestic/global aggregate analyses: - Structural differences across countries (fossil dependence, renewables penetration, supply-chain positioning) will have important cross-border macroeconomic implications. - Changing patterns of trade in energy commodities (fuel, technologies, materials) may substantially impact balance of payments and international financial flows. - Country-level changes can be large even if they nearly cancel out at the global level (Mercure et al., 2018a; 2021). - Political and policy developments highlighting cross-border impacts include: - The European Union’s (EU) Carbon Border Adjustment Mechanism. - The U.S. Inflation Reduction Act. - China’s decision to stop financing coal power plants under the Belt and Road Initiative (Magacho et al., 2023a). - Definition and characterization of the “mid-transition” period: - Transition is defined as transformation of energy, industrial, transport and agricultural socio-technical regimes to reduce greenhouse gas emissions in time to avoid exceeding Paris Agreement limits. - Following Grubert and Hastings-Simon (2022), the “mid-transition” period is the time span during which low-carbon and fossil-based energy and industrial socio-technical regimes are both undergoing rapid transformation, co-exist on a large scale, and operate in a highly contested space. - This period causes new or exacerbates existing instabilities and volatility of global energy markets, with knock-on effects on the global economy. - Literature gaps and need for a consistent analytical framework: - Existing macroeconomic literature often assumes globally coordinated, cost-effective scenarios and sheds little light on structural, cross-border transformation. - There is little systematic analysis of cross-border impacts of decarbonization in a mid-transition context. - Paper’s primary contributions: - Draw on stylized facts to show global decarbonization will very likely change the structure of international trade and capital flows. - Propose an analytical framework based on a system dynamics approach to analyze cross-border effects. - Assess a subset of likely impacts of global decarbonization on output and trade under specific global decarbonization assumptions using the E3ME-FTT global macroeconomic model. ### 2.2 Emerging Cross-Border Risks of the Mid-Transition Period - Cross-border risks can generate or exacerbate instability in economic, political, and financial spheres, potentially undermining the global transition. - Key channels and consequences: - Trade channels: shifts in trade of fuels, technologies, and critical materials affect exporters and importers differently, altering trade balances and exchange rates. - Financial channels: changes in asset valuations (including stranded assets) and cross-border capital flows can create sovereign and financial system vulnerabilities. - Labor and employment channels: decarbonization drives reallocation of employment across sectors and geographies, with fiscal and social implications. - Policy heterogeneity: differences in stringency and pace of climate policies across countries affect cross-border outcomes and the benefits/costs distribution (Dubash, 2021; IMF, 2022). - Distributional implications across major economies (results highlighted in overview): - Among the world’s largest economies, China, India and Japan are likely to benefit the most from the transition relative to a hypothetical baseline of baseline GDP growth. - Russia, Saudi Arabia, the U.S. could be negatively affected relative to that hypothetical baseline. - Scope and caveats: - The analysis highlights cross-border risks without minimizing the major opportunities associated with the transition (economic growth from low-carbon infrastructure, job creation, innovation, environmental and health co-benefits; Bhattacharya et al., 2021). - The counterfactual “no (or failed) transition” scenario would imply far greater cross-border risks stemming from destabilization of the Earth system. - Structure of further analysis (as signposted in the chapter): - Section 3: Stylized facts and taxonomy of country-level cross-border risks. - Section 4: Analysis of potential cross-border impacts from shifting trade, energy, and employment patterns, quantified using E3ME-FTT. - Section 5: Exploration of international macroeconomic and financial spillovers from cross-border mid-transition risks. - Section 6: Summary and reflections on future research. *Source: IMF Working Paper — 2. Stylized Facts to Inform Mid-Transition Cross-Border Risk Analysis (excerpt).* ### 2. Stylized Facts to Inform Mid-Transition Cross- ### 2. Stylized Facts to Inform Mid-Transition Cross- ### Overview - Focus: current technological and structural transformation trends, global macroeconomic and financial patterns observed in the recent past, and specific price dynamics that arise with mid-transition cross-border risk scenarios. - Concept: “mid-transition” — a period when fossil-based and emerging low-carbon energy systems coexist, imposing operational constraints on each other and requiring “decision-making under dynamic and uncertain conditions.” ### 2.1. Stylized Facts for a Mid-transition Period - Parallel trends create potential for higher uncertainty and instability as low-carbon technologies emerge while fossil-based infrastructures persist. - Investment gaps and mismatches: - A substantial low-carbon energy investment shortfall for reaching global average temperature objectives; some estimates put the clean energy shortfall at nearly US$3 trillion per year (IEA, 2022a). - Investment in unabated fossil generation remains high, on a par with its 2016 level (IEA, 2023; IMF, forthcoming a). - Transition dynamics and tipping points: - Low-carbon deployment is self-reinforcing: deployment decreases costs, facilitating further deployment (Way et al., 2022; Mercure 2012; Mercure et al., 2014; Arthur, 1994; Unruh, 2000). - Tipping points toward irreversible diffusion could be near or past for solar energy and electric vehicles (Sharpe and Lenton, 2021; Nijsse et al., 2023; Lam and Mercure, 2022). - Prospect of dominance by solar energy and electric vehicles has stark implications for the value of high-carbon assets (Semieniuk et al., 2022). - Market and policy interactions: - Emerging market and developing economies (EMDEs) could adopt low-carbon technologies even with limited climate policy capacity; diminishing costs through broader adoption could mitigate current account effects of importing low-carbon technologies. - Uncoordinated low-carbon industrial policies (e.g., China’s Five-Year Plans, U.S. Inflation Reduction Act, EU’s “Fit for 55”, REPowerEU, EU Green Deal Industrial Plan, Net Zero Industry Act) interact with geoeconomic fragmentation and can amplify or mitigate it. - Price and stranded-asset risks: - If transition gains momentum, assets—particularly fossil assets—could become stranded if investment exceeds perceived future needs (Pfeiffer et al., 2018; Mercure et al., 2018a, 2021; Semieniuk et al., 2022; van der Ploeg and Rezai, 2020). - Conversely, excessive anticipation of stranding and premature investment collapse could produce price spikes. - Example dynamics: OPEC revised its 2045 global oil demand projection upward from 100.6 to 110 million barrel per day in an interval of only 9 months (OPEC, 2022; Reuters, 2023). - Trade and commodity structure changes: - Declining demand for fossil fuels could reduce trade in fossil commodities without comparable replacement by trade in renewable energy; international trade in green hydrogen will unlikely match today’s volumes of trade in fossil fuels (IRENA, 2022). - Projected growth in trade of low-carbon technology/capital goods and critical minerals (Boer et al., 2021). - Resource constraints and supply risks: - Geological reserves likely sufficient through 2050 (Wang et al., 2023), but demand growth for many critical materials currently far exceeds rate of supply expansion (Miller et al., 2023). - Recent noticeable increases in costs of critical materials could halt or reverse cost declines in some low-carbon technologies (IEA 2021a). - Material substitution faces technological obstacles (e.g., aluminum substituting for cobalt and lithium in batteries remains a major technological challenge). - Geopolitical risks: potential quantitative export restrictions on critical materials by producing countries for national security or strategic competition reasons. ### 2.2. Emerging Cross-Border Risks of the Mid-Transition Period - Country archetypes and cross-border feedbacks: - Five country archetypes defined to characterize cross-border feedbacks through the transition; most countries combine characteristics of two or more archetypes. - Archetype interactions: - Decarbonizing (fossil and cleantech importers) countries affect fossil exporters through volumes and prices, and affect cleantech producers through increased export demand. - Cleantech producers generate cross-border demand for critical material producers. - Archetype mapping uses proxy indicators: - Carbon lock-in index (Andres et al., 2023), exports/imports of critical materials relative to GDP, and a transition outlook index that measures proximity of declining activities to climate-compatible activities. - G20 mapping highlights (scatterplot): - High-carbon locked-in countries (high net fossil exports, low transition outlook): Saudi Arabia, Russia, Australia, Canada, and to a lesser extent Indonesia. - Fossil importer countries: the EU, Japan, Korea, India, and China. - Critical mineral exporter countries (low fossil net exports, critical mineral net exports, low green transition outlook): South Africa, Australia, Brazil (and many outside G20). - Critical mineral importer and cleantech exporter countries (low brown lock-in, critical minerals net imports, high green transition outlook): China, Korea, Japan, the EU; India possibly included with lower green transition outlook. - U.S.: between clusters, with relatively high green transition outlook, small dependency on critical minerals, relatively low dependency on fossil imports, but a relatively large fossil producer and moderate critical mineral producer. - Cross-border flows and macro implications: - Geoeconomic fragmentation could fracture relations between archetypes, incentivize resilience and reshoring, shorten value chains, and both reduce some cross-border risks and amplify others. - Historical context: from 1990 to 2008 world goods exports roughly doubled from 10 percent to 20 percent of world GDP (Shin, 2023); cross-border capital flows increased over this period. - Fragilities stem from extractivism, complex extended value chains and just-in-time production, and financialized corporate management; shocks in upstream sectors can trigger large price volatility and shortages (Weber et al., 2022). - Extent to which discrete relative price shifts become general inflation depends on the monetary policy regime. - Political, fiscal, and conflict risks: - Scarce transition resources can be linked to conflict, especially in fragile states; fossil-exporting countries may face fiscal pressures and social unrest if current account deteriorates and domestic fossil price regimes must be reformed. - Regional fragmentation and insufficient renewable investment increase uncertainty around stranded-fossil-asset expectations; pricing of climate risk in banking remains limited (Beyene et al., 2021; Ongena et al., 2021). - Inflation regime and planetary boundaries: - Possibility of a high-inflation regime (BIS, 2022; Borio et al., 2023) that reduces certainty and deters productive and FDI, potentially swamping policy-related price signals (e.g., carbon pricing). - Higher average temperatures exert persistent inflationary pressure (Kotz et al., 2023); extreme weather and gradual warming increase volatility of domestic and imported inflation through effects on agriculture, housing, and energy. - Other planetary boundaries (e.g., biodiversity) may produce similar inflationary effects (Svartzman et al., 2021; Maurin et al., 2022; Calice et al., 2023). - A switch to a new inflation regime will affect low-carbon investment costs (which tend to be more capital intensive) and produce cross-border ripple effects. - Developmental dilemmas and sufficiency strategies: - Balance-of-payments constraints may force a dilemma between rapid decarbonization and raising income per capita (Valdecantos, 2023); example: South American economies face a “green transition dilemma” where sustaining income growth may imply continued exploitation of natural resources while decarbonization requires lower or negative growth of exports. - Sufficiency strategies (IPCC, 2022) — measures and daily practices that avoid demand for energy, materials, land, and water while delivering wellbeing — could emerge to improve energy and material security, trade balances, and strategic autonomy amid geoeconomic fragmentation and shortages. *Italic: IMF working paper chapter — 2. Stylized Facts to Inform Mid-Transition Cross-* ### 3. Real-Economy Cross-Border Effects in the ### 3. Real-Economy Cross-Border Effects in the Mid-transition Period ### Trade, energy, and employment feedback loops - Deployment of green technology involves strong increasing returns to scale at the national level, causing decreasing energy costs, higher investment levels and possibly technological tipping points. - Costs of low-carbon technologies decline rapidly with cumulative investment, enabling further deployment and reinforced by deployment in other countries. - Financing costs tend to decline with adoption rate as financial actors gain experience in green technologies and markets. - Lack of coordination in fossil production across countries could lead to insufficient or excessive capacity, or both in alternation, potentially causing price and volume volatility. - Cross-border asymmetries: - Fossil-exporting countries: decline in global demand for fossil fuels and associated volatility likely reduce investment in fossil extraction, adversely impacting aggregate investment, employment, and GDP; prompt revaluation of fossil-related assets; lower exports deteriorate current account balances and put devaluation pressure on exchange rates; deterioration in international investment position implies fewer investments of petrodollars in international financial centers. - Cleantech-exporting countries: increased international demand for cleantech capital goods boosts investment, production, output, and employment; improves current account and places appreciation pressure on exchange rates; improves international investment position triggering more investment in international financial centers. - Critical minerals-exporting countries experience analogous effects to cleantech exporters. - Uncertainty: location of low-carbon supply chains is much more uncertain than the location of stranded high-carbon assets; the transition outlook index identifies where cleantech production could arise based on current national capabilities. - Trade substitution nuances: - Future trade in green technology is unlikely to compensate for losses in the trade of fossil fuels because fossil resources are geographically concentrated while renewable energy is available in all countries. - Overall lifetime costs of cleantech are often lower than those of fossil fuels for equivalent energy services and are declining rapidly. - For countries that import both fossil and technology, as cleantech capital costs decline, net impact on imports from technology switching is increasingly negative, all else equal. - Structural financing issues may counterbalance long-run positive effects: fossil investments currently show much higher profitability levels than renewables; EMDEs face much higher cost of capital than developed economies; cost of capital differences can worsen with policy rate increases in large advanced economies leading to flight to safety. ### Employment, fiscal balances, and public debt feedbacks - Aggregate employment implications are uncertain: - Low-carbon investment generally increases employment in domestic manufacturing, construction, electricity and transport in the short to medium run, and decreases employment in high-carbon energy and manufacturing sectors. - Renewables require fewer operational and maintenance jobs per unit of output than fossil fuels, comparable numbers in construction. - In the longer run, productivity increases as capital costs decrease following learning curves may imply fewer workers per unit of energy service. - Net employment impact depends on a country’s industrial structure and whether low-carbon activity created exceeds high-carbon activity lost. - Political economy and fiscal implications: - Decarbonization-induced reallocation of employment and distributional impacts can reduce public acceptance of climate policies; conversely, cleantech investment creating jobs can reinforce acceptance. - Total upfront investment required to reach climate targets is substantially higher than the no-transition counterfactuals because cleantech is capital intensive, potentially lowering fiscal balances and temporarily raising public debt. - Debt issues can arise when debt is issued in foreign currency and the transition increases foreign exchange risk. - Higher low-carbon investments are generally self-sustaining in the medium to long run. ### Modelling trade impacts of decarbonization (E3ME-FTT approach and scenarios) - Model description and key structural features: - E3ME is a global macroeconometric model disaggregated over 70 regions and 43 industrial sectors. - The model’s core is a demand-led input-output and econometric-driven accounting system; demand for final goods and services is estimated based on domestic and import prices and disposable household income. - Disposable income is determined econometrically from employment and wages; investment is determined based on industry expansion needs and prices of capital goods. - Allocation of finance is demand-driven; loans are created based on creditworthiness of projects; new investment does not necessarily crowd out other investment. - Behavioral relationships are estimated through multivariate linear cointegration methods by sector and region on data since 1970. - Trade relationships are established on a bilateral basis; existing trade agreements are taken into account implicitly through the data. - The foreign or domestic origin of investment is not differentiated; stocks of currencies or other assets are not differentiated. - Demand for all energy carriers is estimated in physical units across 22 energy-using sectors. - For fossil fuels, three global pools are assumed (coal, oil, gas); a database covering more than 40,000 oil and gas wells is used to determine economically viable assets and endogenous oil price. - E3ME does not predefine countries as importers or exporters of fossils or critical minerals; these roles emerge from national accounts data. - Sectoral disaggregation, while among the highest, still cannot always separate high- and low-carbon production; mining is not split between critical materials and other materials. - Technology modeling (FTT family): - Technology diffusion follows S-shaped diffusion curves (diffusion of innovations framework). - Technology costs follow endogenous learning-by-doing relationships as a function of cumulative deployment. - The “Future Technology Transformations” (FTT) models cover a total of 88 technologies in power generation, road transport, household heating and steelmaking, covering over 60 percent of global CO2 emissions. - Technology trajectories are calibrated against current diffusion rates of existing technologies. - Rationale for approach: - Use of a demand-driven and technology forecasting approach is important because cross-border impacts primarily stem from changes in demand patterns for high- and low-carbon products driven by behavioral, technology diffusion and innovation processes. - Global optimization models may overlook second- or third-best processes arising from heterogeneous national interests and uncoordinated policies. - Scenarios simulated: - Baseline: energy use trajectory based on existing observed technological trajectories; reproduces current decarbonization trends but not land-use nor non-energy use of fossil fuels; current policies implicitly assumed maintained. - Decarbonization scenario: achieves net-zero emissions worldwide shortly after 2050 via additional policies (subsidies, investment programs, energy efficiency regulations and a carbon price), designed regionally according to national circumstances and calibrated where possible to existing policy frameworks. - Model assumptions: reproduces key features of an orderly rapid transition by assumption and does not explicitly include endogenous processes of instability through the mid-transition period; electric vehicles and solar diffusion trends make fossil use peak and decline by 2035 or before in all scenarios, including the baseline. - Note: Using any alternate baseline in which fossil fuels do not peak would intensify cross-border impacts discussed; current modeling suggests such non-peaking baselines are unlikely. - Modeling caveats and limits: - The model excludes emerging trade barriers related to the conflict in Ukraine. - The model does not explicitly model instability in the mid-transition but models imbalances (notably large changes in trade balances) that could give rise to such instability. - Purpose: - To model main economic imbalances that could give rise to cross-border risks in a global decarbonization scenario, focusing on trade, exchange rates, employment, fiscal balances, and international investment positions. *Source: IMF Working Paper — chapter "3. Real-Economy Cross-Border Effects in the Mid-transition Period" (wpiea2023184-print-pdf)* ### 3.3 Discussion of Results ### 3.3 Discussion of Results ### Key mid-transition external macroeconomic results for G20 - Energy-related exports decline relative to total baseline exports in the net-zero scenario versus the current trajectory, with effects highly concentrated in large fossil fuel exporters: Canada, Russia, Saudi Arabia, the U.S. (for oil), and South Africa (for coal). - Timing differences: impacts on Saudi Arabia are delayed while impacts on Canada, Russia and the U.S. are accelerated, reflecting relative competitiveness in oil and gas markets from modeling individual oil and gas assets worldwide. - Aggregate model results on GDP, employment, exports, and emissions are reported separately (Figure A.3 in Annex II). ### Trade balances and GDP impacts (figures and magnitudes) - Figure 7 (Energy Exports Change Relative to Total Baseline Exports) axis tick labels shown: -30, -25, -20, -15, -10, -5, 0, 5; country markers shown for 2030 2040 2050. - Figure 8 (Trade Balance Change Relative to Baseline GDP) axis tick labels shown: -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2; country markers shown for 2030 2040 2050. - Figure 9 (Change in GDP Relative to Baseline) axis tick labels shown: -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10; country markers shown for 2030 2040 2050. - Overall patterns: - Most countries improve their trade balances through the transition, except large fossil producers, which see worsening trade balances. - Fossil exporters are negatively affected in GDP terms, while fossil importers largely benefit. - China, the EU and India could see trade balances improve; India stands to benefit the most, relative to GDP. ### Drivers and structural explanations - Global distribution of renewables and falling solar costs (reference to parity with lowest cost fossil-based electricity generation) imply renewables are widely available; electricity trade across borders is limited by substantial and expensive transmission infrastructure. - Losses of fossil exports and imports are not offset by cross-border trade of electricity or other low-carbon energy carriers. - A shift from relatively inefficient fossil combustion technology toward more efficient non-thermal options results in total primary energy demand growth largely stabilizing in absolute energy units while energy services continue to grow. ### Fossil volumes, reserves, and OPEC dynamics - Volumes of fossil demanded until 2050 become limited and compare relatively closely with available reserves in OPEC countries, where extraction is on average most competitive. - Available reserves exceed the range of possible total demand for oil and gas in scenarios achieving a 2°C target. - Geopolitical analysis suggests OPEC producers could maintain or increase current oil and gas output between now and 2050, increasing market share at the expense of non-OPEC producers; this could push less-competitive extraction sites (deep offshore, arctic, shale oil/gas, tar sands) out of markets. - Current simulation assumption: OPEC continues present role as market makers preserving price stability using production quotas. - Alternative scenario: if OPEC increases production to expand market share and lower international oil and gas prices, all impacts shown in Figures 7-9 are intensified by an order of 10 percent. ### Structural economic adjustments and fiscal consequences - Many economies evolve away from primary and secondary industries toward services through 2050; the transition could accelerate this shift. - In fossil-producing countries, substantial levies on fossil production typically finance sizable fractions of government expenditure across many sectors. - Loss of royalties implies potentially large reductions in public spending and reduced disposable income spent on final goods and services (model assumes no compensatory deficit spending by governments). - Wage income from fossil fuel workers supports regional activities, notably services, which could be significantly affected; hence transition impacts extend beyond Industry to services. ### Scenario timing and robustness - Under an assumption of return to global geopolitics comparable to the situation prior to the conflict in Ukraine: - Canada, Russia and the U.S. would see fossil exports decline very rapidly and their trade balances could deteriorate within a decade. - OPEC countries may not see deterioration in their trade balances until after 2035. - China, the EU and India likely see improved trade balances and output growth; India benefits most relative to GDP. - Model energy demand trends are relatively robust to assumptions about global energy market instability; energy supply trends are less robust because unpredictable trade restrictions, conflict, production decisions, and strategic reserve use could affect prices and volumes. ### Key uncertainties and constraints - New primary commodity price cycles: IEA (2022b) suggests current price increases for critical materials for low-carbon technologies may be overcompensating learning-by-doing cost reductions; model implicitly assumes this is temporary and new supplies will release constraints. - Changes in existing trade agreements (WTO, bilateral treaties) are difficult to model and may constrain the ease of trading new technologies worldwide, but would not likely change modeled losses of trade in fossil fuels if losses originate from demand declines. ### Cross-border financial risks from stranded assets and ownership structures - Large amounts of fossil infrastructure are at risk of premature decommissioning given inconsistency between committed emissions from invested capital and carbon budgets; trillions of U.S. dollars of investments in fossil assets could be at risk of stranding if investor expectations do not align with low-carbon scenarios. - Physical capital operable only with fossil fuels (e.g., certain steel and concrete manufacturing methods, airplanes) and fossil-dependent supply chains are directly at risk; other industries may be indirectly at risk from policy and regulation changes. - Ownership mismatch: owners of at-risk assets frequently have parent organizations or shareholders domiciled abroad, creating cross-border systemic risk. - Finance-based emissions: since 2015 most coal-fired power plants constructed in Asia have financing sources located abroad, raising finance-based (as opposed to territorial) emissions of many advanced economies by up to 10 percent (17 percent) in the U.S. (Japan). - Cross-border equity ownership implications: in a medium scenario of oil and gas asset stranding, OECD country corporate shareholders are exposed to 55 percent of losses even though only 36 percent of physical assets are likely to be stranded in OECD countries. - Second-round liquidity effects: assets used as collateral for cross-border loans could trigger sell-offs as creditors offset emerging collateral risks. - Asset managers and public actors with large exposures to real-economy assets (energy, transportation, water, farmland, housing) face potential large transition-induced losses; sovereign wealth funds’ changes in “carbon portfolios” could have cross-border impacts. - Exchange rate channel: renewables projects financed in foreign currency but generating domestic-currency revenues expose international investors to currency risk and regulatory risk, potentially generating adverse feedbacks between bank lending and insurance provision. - Fossil asset retirement obligations and related legal/environmental risks could increase with climate policy implementation, affecting valuation and competitiveness of major oil and gas companies and the development of low-carbon value chains (example: EU likely exit from the Energy Charter Treaty). *IMF Working Paper — 3.3 Discussion of Results* ### 4.2 Sovereign Risk and The International Financial System ### 4.2 Sovereign Risk and The International Financial System ### Credit ratings and sovereign debt costs - Markets have begun partially pricing physical and transition risks, increasing the cost of debt in climate vulnerable countries. - Transition impacts can cause fundamental and enduring structural changes that erode fiscal revenue and require large-scale government spending. - Example estimate: oil exporters in Latin American and the Caribbean stand to lose up to US$3 trillion in royalties by 2035. - Potential downgrades by credit rating agencies could make it costlier for sovereigns to tap international debt markets. - A failure to reduce reliance on carbon-intensive activities could raise pressures on EMDE sovereign borrowing costs by lowering investor appetite for EMDE assets. - Declining exports and current account balances may coincide with greater need for external finance, worsening sovereign funding pressures. - Sovereign debt crises in climate-vulnerable countries could erode their ability to backstop domestic banks through loss of access to international debt markets or central bank swap lines, potentially producing simultaneous financial, fiscal, and balance of payments crises across countries. ### International investment position and foreign exchange reserves - Fossil exporting countries may experience a deterioration of current account balances and a worsening of their international investment position as oil revenues could peak and decline with diffusion of electric vehicles. - Fossil producers may lose export revenues and see declines in foreign direct investment in the fossil sector; some may be forced to liquidate international assets to finance deficits. - The disruption to international financial markets depends on the speed and scale of these dynamics. - A deterioration of fossil exporters’ international investment position could affect flows of petrodollars into international financial centers, notably the U.S. - Cited estimate: without substantial foreign inflows into U.S. government bonds, the 10-year Treasury yield could be 80 basis points higher. - New international investment flows may emerge towards cleantech and critical mineral exporters; these "sunrise" flows may not replace "sunset" flows nor use the same currencies, with implications for the international reserve system and the status of the dollar. ### Cross-border links between sovereign debt crises, extractivism, and transition risks - IMF estimates: about 15 percent of low-income countries are currently in debt distress, and 45 percent are at high risk of debt distress. - Among emerging economies, 25 percent are at high risk and facing "default-like" borrowing spreads. - Widespread lack of fiscal space and public and external debt pressures create significant needs for foreign exchange, pressuring developing countries to open to FDI, particularly in profitable extractive sectors such as fossil fuels or mining. - These dynamics illustrate cross-border connections between external debt problems in developing economies and the build-up of transition risks. ### Monetary policy and cross-border mid-transition risks - Monetary tightening in response to inflation shocks could slow investment in renewables and favor fossil fuels by raising investors' hurdle rates for capital-intensive renewable projects. - Low-carbon technologies have a higher capital-to-operational expenditure ratio; rising yields adversely affect their relative profitability. - Financing costs are a major component of renewables’ levelized cost of electricity (LCOE) in EMDEs: - Financing costs accounted for around half of the LCOEs of a solar PV plant in EMDEs in 2021, compared to only 25‐30 percent in AEs and China. - Monetary tightening in AEs could cause capital outflows from EMDEs, delaying transitions and raising transition risks. - Expectation of bailouts by major central banks can indirectly generate cross-border transition risks by: - Acting as price-insensitive buyers and supporting low-creditworthiness companies, altering market prices of fossil company debt and socializing losses across jurisdictions. - Signaling to foreign investors that high-carbon activities are a safe bet because profits are privatized while losses are socialized. - Preliminary analysis (Annex III) estimates the implied temperature rise (ITR) of asset purchases during the COVID-19 crisis: - Fed’s SMCCF ETF portfolio: 3.1°C implied temperature rise. - ECB’s CSPP purchases: 2.3°C implied temperature rise. - Note: There are 16 ETFs under SMCCF and 14 of them have implied temperature rise data in MSCI dataset. ### Global climate finance architecture and related policies - Four main types of policy actions identified for shaping the financial sector toward low-carbon transition: - Green regulatory policies (including green supporting factors and dirty penalizing factors). - Green portfolio rewards/penalties for financial institutions by share of lending to low-carbon activities. - Green monetary policies that orient central bank operations by their environmental impact. - Public co-financing of green investment. - Prudential regulation should remain risk-focused and not substitute for effective government climate policy. - Mobilization of finance from developed countries to support developing countries requires grants, concessional loans, and guarantees. - Policies targeting domestic financial sectors could generate balance of payments sustainability issues due to import intensity of transition investments and exchange rate effects. - Higher financial risks associated with climate-related investments imply greater reliance on leverage, exposing domestic financial sectors to sudden tightening in global financial conditions. ### Proposed financing instruments and constraints - Green Special Drawing Rights (SDR) allocations could increase recipient countries’ stock of foreign exchange reserves and extend room for green financial interventions without augmenting external vulnerability. - The Resilience and Sustainability Trust reflects part of this approach. - Debt for climate swaps: creditors provide debt relief in return for government commitments to decarbonize, invest in climate-resilient infrastructure, or protect biodiversity. - Contingent debt instruments (e.g., climate resilient debt clauses) can automatically suspend interest payments after a climate shock reaches a pre-defined threshold. - Broader coordinated public sovereign guarantee mechanisms based on the social value of mitigation action have been proposed. - Scaling up these instruments faces many technical, financial, and governance-related challenges. ### Conclusion: cross-border mid-transition risks, taxonomy, and policy implications - Mid-transition instability is possible in the short to medium run due to ongoing changes committed by past climate policies; instability could be prolonged into a "mid-transition trap" if international coordination and policy instruments fail. - Paper contributions: - Characterization of mid-transition cross-border risks in technological, trade, and financial domains, highlighting path dependence, coexistence of fossil and low-carbon systems, and potential resource constraints. - Taxonomy of cross-border risks across five dimensions: exports/imports of fossil fuels; transition outlook (including clean tech trade); and exports of raw critical minerals, informed by clusters of G20 countries’ brown lock-in indices and critical minerals trade. - Framework for analyzing transmission channels including energy, trade, employment, sovereign, and financial risk feedback loops; first quantification attempt using the E3ME-FTT model showing very large cross-border changes by 2050 for certain G20 members and Spain, notably Argentina, Brazil, the Russian Federation, Saudi Arabia, South Africa, and the U.S., with heterogeneous macroeconomic effects depending on transition outlook and green production capacity. - Key policy implications: - International cooperation on economic, climate, and development policies (notably the G20 and the climate COPs) must emphasize cross-border dimensions of transition dynamics. - Develop indicators of cross-border risks and opportunities beyond the simplified taxonomy presented. - National policy agendas should include focus on mitigating cross-border transition risks regionally and internationally. - Further research required on linkages between macroeconomic and financial channels, including external debt, monetary policy, tightening global financial conditions, and systemic cross-border transition financial risks (e.g., buildup of fossil infrastructure and assets in many EMDEs). - Critical open questions raised: - What role should financial regulation, climate information architecture, and the global financial safety net play in mitigating cross-border transition risks? - Do EMDE exporters face a risk of a "critical materials resource curse" and related macrofinancial risks? - What role can international policy cooperation play in stabilizing key commodity prices and organizing critical metals markets? - What are the implications of continued geoeconomic fragmentation during the mid-transition, including the risk the transition could destabilize or stop altogether, producing a "mid-transition trap"? *Source: IMF Working Paper — "Cross-Border Risks of a Global Economy in Mid-Transition", section 4.2 and related excerpts.* ### Annex I. The E3ME-FTT-GENIE Integrated ### Annex I. The E3ME-FTT-GENIE Integrated Assessment Model ### Overview - E3ME-FTT-GENIE is an integrated energy-technology-economy-climate simulation model used to assess impacts of policies for stakeholders including governments (EU Commission, national governments). - The model links: - FTT (Future Technology Transformations): detailed technology diffusion dynamics for carbon-intensive sectors. - E3ME (Energy-Economy-Environment MacroEconometric model): detailed and highly disaggregated macroeconomics. - GENIE (Grid Enabled Integrated Earth system model): a climate and carbon cycle simulation of intermediate complexity. - For a complete description of model equations and climate simulation integration, refer to Mercure et al.(2018a) (climate integration omitted here). ### Macroeconomic evolution in E3ME - Model architecture and data: - Demand-driven macroeconometric model based on a social accounting matrix with input-output and bilateral trade relationships. - Parameterized on time series from 1970 to the present. - Disaggregation: 70 regions (including all G20 nations) and 43 (70) sectors of industry — 43 sectors for countries outside the EU, 70 sectors for countries inside the EU. - Econometric relationships project variables up to 2070. - Behavioral assumptions and mechanisms: - Not based on production functions nor utility optimization; does not assume full employment of labor, physical and financial capital. - Consumption by product type determined econometrically from prices, disposable income, population, and expenditure patterns. - Input-output relationships determine final and intermediate production and demand for investment goods. - Investment determined econometrically from past economic activity, prices of capital assets, and capacity use. - Employment and hours worked determined from economic activity. - Imports and exports determined by price differentials between domestic and foreign goods by sector. - Innovation and learning: - Technology progress factors determined from cumulated past investment by sector; accumulation of capital leads to production cost reductions via a sector-wide learning-by-doing regression parameter. - GDP calculated as sum of value added across the economy; sectoral output calibrated in baseline to match OECD and national projections. ### Energy sector module in E3ME - Physical energy accounting: - Energy demand estimated in physical units by energy carrier for all sectors and fuel users, using energy balance time series from the International Energy Agency. - Final demand determined for 22 types of final energy users and 12 types of fuels (including oil, coal, gas, electricity, biofuels). - Allows accurate estimation of greenhouse gas emissions in all scenarios. - Econometric estimation: - Energy use estimated based on sectoral economic activity and substitution between sectors. ### Technology diffusion in FTT - Purpose and scope: - FTT represents technological diffusion where technological change is the major driver of substitution, addressing limitations of elasticity-based substitution approaches. - Technology coverage: - Current representation of 88 technologies: - 24 in power generation - 30 in road transport - 10 in household heating - 24 routes in steelmaking - Examples: coal plants and solar panels (power), petrol and electric vehicles (road), gas boilers and heat pumps (heating). - Vintage capital and turnover: - Vintage capital model: fleets of technological items age and depreciate with turnover determined by technology-specific survival functions. - Example survival times: cars survive on average for 11 years; coal plants survive for 40 years. - Implication: over 25 years, vehicle fleet turns over entirely; power generation technological change is slower. - Agent choice and diffusion dynamics: - Heterogeneous agents make technology comparisons; availability of technologies to agents is assumed proportional to market prevalence (peer or visual influence). - Peer influence drives S-shaped diffusion profiles; uptake rates calibrated to historical diffusion. - Agent choice uses levelized cost metrics (e.g., $/MWh, $ per km driven). - Each technology characterized by learning-by-doing rate, reducing costs with cumulative investment. - Policy interactions: - Exogenous policies influence uptake: technology-specific subsidies, carbon price/tax, other taxes, bans, regulations, public/private procurement/investment. - Model coupling: - E3ME supplies FTT with total demand by FTT sector (power, transport, heat, steelmaking). - FTT supplies E3ME with prices, investment, fuel use by fuel type, and public income/expenditure from policy initiatives. - Electricity market representation: - Power model includes electricity storage, capacity factors, load bands, and output allocation according to auction by the network regulator. - No systematic studies of alternate market clearing rules across countries; assumption that electricity prices approximately reflect average costs of production across the technology fleet. ### Fossil fuel asset module - Database and method: - Uses a detailed database of fossil fuel stocks by region distributed along a production cost variable. - Oil and gas assets sourced from the Rystad database documenting over 40,000 oil and gas assets worldwide; Rystad provides 2P reserves and resources and breakeven cost per asset. - Coal reserves determined from various sources; less-detailed treatment due to ubiquity of low-cost coal. - Production decision rule: - Model assumes each asset produces if and only if it is profitable in a given period. - Uses Rystad data to determine which assets produce or remain idle according to oil and gas prices, searching the database to find prices that clear demand each simulated year. - Implications for scenarios: - In peaking and declining oil demand scenarios, high breakeven cost assets can stop production and become stranded (example: tar sands in Canada), while low breakeven cost assets remain until depleted (example: conventional oil in OPEC). - Enables detailed production profiles for each E3ME country per scenario, strongly affecting economic activity for oil producers via exports and trade balances and indirectly affecting importers by reducing imports. ### Climate policies and scenarios - Policy instruments modeled: - Carbon taxes, fuel taxes, technology subsidies, public investments, fuel blend mandates, vehicle mandates, scrappage schemes. - Complementarity and policy mix insights: - Non-linear technology diffusion (FTT) produces complementarity effects: combined policies often yield outcomes greater than the sum of individual policies. - Carbon taxes and technology subsidies work well with mandates; mandates expand consumer choices while taxes/subsidies stabilize choices. - Taxes alone work less well if consumer choice is limited; mandates alone work less well if technologies fail to become cost-competitive. - Technology compositions differ substantially across countries; effective policy mixes vary by country circumstances. - Cross-sectoral and sectoral policies (applied across 71 countries with independent mixes): - Cross-sectoral: - Carbon price that gradually increases over time to around $200/tCO2 in 2050 and covers the power sector and industrial activities, but not personal transport nor residential heat. - Energy efficiency regulations for sectors not modelled in FTT. - Power sector: - Feed-in tariffs (or contracts for difference) for wind power; usually no policy needed for solar. - Capital cost subsidies for geothermal, hydro, carbon capture, nuclear and other low-carbon options. - Implicit assumption that market regulations change to allow renewables fair remuneration (e.g., reforming marginal cost pricing). - Ban on building new coal plants by 2030, and for gas plants by 2040. - Road transport: - Ownership/purchase taxes for conventional vehicles. - Subsidies on electric vehicles (hydrogen vehicles not currently modelled). - Electric vehicle mandates in early years to increase numbers on roads. - Biofuel blends. - Bans on conventional vehicles in 2030 or on dates announced by countries. - Assumption that charging infrastructure diffuses at the same pace as electric vehicles. - Household heating: - Heating fuel taxes. - Subsidies on heat pumps, solar heaters and other low-carbon options. - Mandates on heat pumps and other low-carbon options. - Steelmaking: - Public investment in hydrogen steel demonstration plants to attract private investment (modelled similarly to a mandate, requiring capacity build partly funded by public sector). - Capital cost subsidies. - Assumption of declining hydrogen input costs. - Scenario construction and net-zero targets: - Policy stringency adjusted to achieve net-zero targets where pledged: - 2050 for the EU, Japan and Korea. - 2060 for China. - 2070 for India. - For the rest of the world, policy stringency adjusted to achieve net-zero by between 2050 and 2060. - Note: Many equivalent policy mixes can achieve these reductions in the model; carbon pricing on its own does not achieve those targets. ### Composition of macroeconomic changes - Five feedbacks from policy instruments to output in E3ME-FTT: 1. Changes in energy prices (e.g., from carbon pricing) may raise electricity and other energy carrier prices, reducing industry output and consumption. 2. Investment feedback: low-carbon technologies are capital-intensive and can increase productivity once cost parity is achieved; additional investment increases employment in construction and manufacturing and increases output; includes losses of investment in high-carbon sectors. 3. Subsidies impact government balances and are financed by changes in other taxes (e.g., on employment), implying reduced expenditure elsewhere and potential reductions in employment and output. 4. Carbon pricing raises substantial domestic resources, offsetting subsidy-financing effects; also includes losses of tax income such as royalties from fossil fuel extraction. 5. Trade changes: shifts in trade of carbon-intensive and low-carbon products affect domestic employment and output; fossil fuel producers are negatively affected by reduced demand from importers, while importers may benefit from reduced imports assuming resources not spent on imports are spent domestically. - Heterogeneity of dominant feedbacks: - Dominant feedbacks depend on country context: - Investment feedback often dominates in middle-income fossil fuel importers (example groups: India and China). - Trade effects dominate for fossil fuel exporters. - Examples of aggregate outcomes: - EU, China, India: "double dividend" — investment-led construction booms alongside large reductions in imports, with money assumed spent domestically. - Canada, Russia, OPEC and to some degree the US: "double loss" — low or negative investment feedback relative to baseline and large negative export-driven income losses; loss of royalty income can be substantial relative to government expenditure and, assuming balanced budgets, lead to reduced government expenditure. - Scope limitation: - The analysis models effects of decarbonization on the economy but does not model impacts of climate change itself; the baseline excludes output losses due to climate impacts. Including climate impacts would systematically increase aggregate output in decarbonization scenarios relative to baseline. *Source: Annex I. The E3ME-FTT-GENIE Integrated Assessment Model (from the provided IMF content).* ### Annex III. Estimating the Implied Temperature ### Annex III. Estimating the Implied Temperature Rise of Fed and ECB Corporate Asset Purchases During the COVID-19 Crisis ### Policy facilities and scope - The Fed Board authorized two facilities: the Primary Market Corporate Credit Facility (PMCCF) and the Secondary Market Corporate Credit Facility (SMCCF). - The CARES Act allocated US$25 billion to the SMCCF and US$50 billion to the PMCCF (Federal Reserve Bank of New York, 2021). - SMCCF purchases included outstanding corporate bonds of Eligible Issuers and ETFs in the secondary market, with SMCCF’s ETFs holdings accounting for over half of the SMCCF portfolio. - The Fed also established the Main Street Lending Program (MSLP) to support lending to small and medium-sized for-profit firms and non-profit organizations; this program was not assessed due to data limitations. - The analysis focuses on two facilities: the Fed’s SMCCF and the ECB’s Corporate Sector Purchase Programme (CSPP). ### Methodology for estimating Implied Temperature Rise (ITR) - Metric used: MSCI’s “Implied Temperature Rise” (ITR) metric, which indicates “how companies and investment portfolios align to global climate targets” (MSCI, 2022). - Conceptual basis: ITR methodology is based on carbon budgets and extrapolates the global implied temperature rise at a 2100 horizon “as if the whole economy ‘overshot/undershot’ its budget in the same way as the given company (or portfolio) ‘overshoots/undershoot[s]’ its specific company-specific carbon budget” (MSCI, 2022). - Caveats: ITR metrics rely on several key assumptions, including measurement and estimation of climate performance, uncertainties in scenarios, disaggregation of macro trajectories to micro benchmarks, and calculation of temperature alignment (Raynaud et al., 2020, 60). - SMCCF methodology: - Match the list of indexes and firms whose bonds were purchased under the SMCCF with the ITR estimated for them by MSCI. - Plot a density histogram representing the distribution of ITRs, weighted by the amount of asset purchased, for both the ETFs and corporate bonds purchased under the SMCCF. - CSPP methodology: - Because the ECB does not provide publicly-available data on amounts of corporate bonds purchased under the CSPP, obtain the list of corporate bonds eligible for the CSPP at the beginning and the end of the increase in asset purchases associated with the COVID-19 response, based on ECB data on the history of cumulative net asset purchases under the ECB’s Asset Purchase Programme. - Concatenate firm names to obtain as comprehensive a list as possible of CSPP-eligible bonds. - Plot a histogram of the ITRs of these bonds and compute the simple average of these ITRs. ### Key findings and statistics - Purchase-amount-weighted ITR for combined SMCCF purchases: 3.1°C. - Purchase-amount-weighted ITR for corporate bonds purchased under the SMCCF: 2.5°C. - Simple average ITR of firms whose bonds are CSPP-eligible: 2.3°C. - Interpretation: Fed and ECB corporate bond purchases during the COVID-19 crisis implied a high temperature rise, consistent with existing literature (Matikainen et al., 2017; Dafermos et al., 2020). - Policy signal: Asset purchases at below-market rates can amount to a bailout-like profit transfer when a sector is in trouble, signaling that risks of continued investment in high-emission assets are limited because their value will be backstopped when shocks materialize. ### Cross-border linkages and examples - The Fed’s SMCCF purchased bonds from multinational oil and gas companies (including most oil supermajors) and high-yield corporate bond ETFs that included foreign companies subject to high credit risk. - Example: Canadian oil company MEG Energy had negative earnings in 2019 and 2018, a stock with a -33 percent five-year annualized return, and a B3 rating for its unsecured bonds (Braham, 2020). *Source: IMF Working Paper — Annex III. Estimating the Implied Temperature Rise of Fed and ECB Corporate Asset Purchases During the COVID-19 Crisis.* ### 105530. https://doi.org/10.1016/j.eneco.2021.105530 ### wpiea2023184-print-pdf - 105530. https://doi.org/10.1016/j.eneco.2021.105530 ### Key thematic areas represented - Ownership, asset management, and rentier dynamics in the economy - Christophers, B. (2020), Rentier Capitalism: Who Owns the Economy, and Who Pays for It? London: Verso. - Christophers, B. (2023). Our Lives in Their Portfolios: Why Asset Managers Own the World. London: Verso. - Energy transition technology, costs, and market prospects - Christophers, B. (2022), “Taking Renewables to Market: Prospects for the After-Subsidy Energy Transition,” Antipode, 1-26. - Egli, F., Steffen, B., & Schmidt, T. S. (2018), A dynamic analysis of financing conditions for renewable energy technologies. Nature Energy, 1. - IEA (2010a). World Energy Outlook. Paris: International Energy Agency. - IEA (2010b), Projected Costs of Generating Electricity. 2010 Edition. Paris: International Energy Agency. - IEA (2015), Projected Costs of Generating Electricity. 2015 Edition. Paris: International Energy Agency. - IEA (2020a), Projected Costs of Generating Electricity. 2020 Edition. Paris: International Energy Agency. - IEA (2020b), IEA Energy Technology Perspectives 2020. Paris: International Energy Agency. - IEA (2020c), The Oil and Gas Industry in Energy Transitions. Paris: International Energy Agency. - IEA (2021a), The Role of Critical Minerals in Clean Energy Transitions. World Energy Outlook Special Report. Paris: International Energy Agency. - IEA (2021b), “The cost of capital in clean energy transitions.” Paris: International Energy Agency. - IEA (2022a), World Energy Outlook 2022. Paris: International Energy Agency. - IEA (2022b), Climate Resilience for Energy Security. Paris: International Energy Agency. - IEA (2023), World Energy Investment 2023. Paris: International Energy Agency. ### Climate-finance and macro-financial interactions - Climate risks, financial stability, and monetary/financial policy responses - Dafermos, Y., M. Nikolaidi, and G. Galanis (2018), “Climate Change, Financial Stability and Monetary Policy.” Ecological Economics 152, 219–234. - Dafermos, Y., D. Gabor, M. Nikolaidi, A. Pawloff, A., and F. van Lerven (2020), “Decarbonising is easy: Beyond market neutrality in the ECB’s corporate QE.” London: New Economics Foundation. - ECB (2021), Climate-Related Risk and Financial Stability. Frankfurt: European Central Bank. - IMF (2021), Global Financial Stability Report, Chapter 3, “Investment Funds: Fostering the Transition to a Green Economy,” October. Washington, D.C.: International Monetary Fund. - NGFS (2023), Stocktake on Financial Institutions’ Transition Plans and their Relevance to Micro-prudential Authorities. Paris: Network of Central Banks and Financial Supervisors for Greening the Financial System. - Matikainen, S., E. Campiglio, and D. Zenghelis (2017) “The climate impact of quantitative easing”, Grantham Research Institute on Climate Change and the Environment Policy Paper. London: London School of Economics and Political Science. - Monasterolo, I., A. Mandel, S. Battiston, A. Mazzochetti, K. Oppermann, J. Coony, S. Stretton, F. Stewart, and N. Dunz (2022), “The Role of Green Financial Sector Initiatives in the Low-Carbon Transition: A Theory of Change,” World Bank Policy Research Working Paper 10181. Washington, D.C.: World Bank. ### Modeling, scenarios, and technology diffusion - Integrated assessment, sectoral and macroeconomic modeling, and emission budgets - Mercure, J.F. (2012), FTT: Power: A global model of the power sector with induced technological change and natural resource depletion. Energy Policy, 48, pp.799-811. - Mercure, J.-F. and P. Salas (2012), “An assessment of global energy resource economic potentials,” Energy 46(1), pp.322-336. - Mercure, J.-F., H. Pollitt, U. Chewpreecha, P. Salas, A. M. Foley, P. B. Holden, and N. R. Edwards (2014). “The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector.” Energy Policy, 73, 686-700. - Mercure, J.-F., et al. (2018a), “Environmental Impact Assessment for Climate Change Policy with the Simulation-based Integrated Assessment Model E3ME-FTT-GENIE,” Energy Strategy Reviews 20, 195–208. - Mercure, J.-F., et al. (2018b), “Macroeconomic Impact of Stranded Fossil Fuel Assets,” Nature Climate Change 8(7), 588–593. - Mercure, J.-F., F. Knobloch, H. Pollitt, L. Paroussos, S.S. Scrieciu, and R. Lewney (2019), “Modelling Innovation and the Macroeconomics of Low-carbon Transitions: Theory, Perspectives and Practical Use,” Climate Policy 19(8), 1019–1037. - Mercure, J.-F., et al. (2021), “Reframing Incentives for Climate Policy Action,” Nature Energy, 6, 1133–1143. - Grubler, A., C. Wilson, N. Bento, B. Boza-Kiss, V. Krey, D. L. McCollum, and H. Valin (2018). “A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies.” Nature Energy 3(6), 515-527. - Meinshausen, M., et al. (2009), “Greenhouse-Gas Emission Targets for Limiting Global Warming to 2 °C,” Nature 458 (7242): 1158–1162. - Millar, R. J., et al. (2017), “Emission budgets and pathways consistent with limiting warming to 1.5°C,” Nature Geoscience, 10(10), 741-747. - Rogelj, J., et al. (2018), “Scenarios towards limiting global mean temperature increase below 1.5°C.” Nature Climate Change, 8(4), 325-332. - Riahi, K., et al. (2022), “Mitigation pathways compatible with long-term goals.” In Shukla, P. R. (et al.) (eds.). Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge University Press. pp. 295-408. - IPCC (2018), Summary for Policymakers. In: Global Warming of 1.5°C. - IPCC (2022), Assessment Report 6, Working Group III, Climate Change 2022: Mitigation of Climate Change. - IPCC (2023), Assessment Report 6, Synthesis Report. Summary for Policy Makers. https://report.ipcc.ch/ar6syr/pdf/IPCC_AR6_SYR_SPM.pdf ### Cross-border, trade, and supply-chain spillovers - Trade policy, external adjustment, and cross-border climate spillovers - Copeland, B. R., J. S. Shapiro, and M. S. Taylor (2022), “Globalization and the Environment,” in Handbook of International Economics, vol. 5. - Feng, A., and H. Li (2021), “We Are All in the Same Boat: Cross-Border Spillovers of Climate Risk through International Trade and Supply Chain,” IMF Working Paper No. 2021/013. Washington, D.C.: International Monetary Fund. - Magacho, G., E. Espagne, and A. Godin (2023a) "Impacts of CBAM on EU trade partners: consequences for developing countries." Climate Policy. - Magacho, G., E. Espagne, A. Godin, A. Mantes, and D. Yilmaz (2023b). Macroeconomic exposure of developing economies to low-carbon transition. World Development, 167, 106231. - Shapiro, J. S. (2021), “The Environmental Bias of Trade Policy,” The Quarterly Journal of Economics 136(2), 831–886. - Schenker, O. (2012), “Exchanging Goods and Damages: The Role of Trade on the Distribution of Climate Change Costs,” Environmental & Resource Economics 54(2), 261–282. ### Policy design, carbon pricing, and fiscal/monetary considerations - Carbon pricing, fiscal frameworks, and methods for integrating climate risks into macro forecasting - King, M., Tarbush, B., & Teytelboym, A. (2019). Targeted carbon tax reforms. European Economic Review, 119, 526‐547. - Devulder, A., and N. Lisack (2020), Carbon tax in a production network : Propagation and sectoral incidence (Working Paper No 760).Francee de France. https://publications.banque-france.fr/en/carbon-tax-production-network-propagation-and-sectoral-incidence - Hourcade, J.-C., A. Pottier, and E. Espagne (2018), Social value of mitigation activities and forms of carbon pricing, International Economics, 155, 8-18. - Hourcade, J.-C., D. Dasgupta, and F. Ghersi (2021), Accelerating the speed and scale of climate finance in the post-pandemic context. Climate Policy, 21(10), 1383-1397. - Council of Economic Advisers and Office of Management and Budget (2023), “Methodologies and Considerations for Integrating the Physical and Transition Risks of Climate Change into Macroeconomic Forecasting for the President’s Budget,” White Paper, March 13. - Prasad, A., E. Loukoianova, A. X. Feng, and W. Oman (2022), “Mobilizing Private Climate Financing in Emerging Market and Developing Economies,” IMF Staff Climate Note 2022/007. Washington, D.C.: International Monetary Fund. - ICMA (2022), ICMA publishes new climate resilient debt clauses to facilitate sovereign debt relief and financial stability. News. International Capital Market Association. 9 November. ### Empirical and dataset resources cited - Databases, empirical methods, and measurement approaches - Nardo, M., N. Ndacyayisenga, A. Pagano, and S. Zeugner (2017), “Finflows: Database for Bilateral Financial Investment Stocks and Flows,” JRC Technical Reports. https://doi.org/10.2760/172684 - Rystad (2021), UCube Dataset. https://www.snowflake.com/datasets/rystad-energy-ucube-global-asset-level-oil-and-gas-database/ - MSCI (2022), “Implied Temperature Rise Methodology Summary,” MSCI ESG Research, September 2022. - Manych, N., J. C. Steckel, and M. Jakob (2021), “Finance-Based Accounting of Coal Emissions,” Environmental Research Letters 16(4). https://doi.org/10.1088/1748-9326/abd972 *Source: wpiea2023184-print-pdf - 105530. https://doi.org/10.1016/j.eneco.2021.105530* ### 637. https://doi.org/10.7930/NCA4.2018.CH16 ### Cross-Border Risks of a Global Economy in Mid-Transition ### Selected bibliographic entries (pages 53–54) - 637. https://doi.org/10.7930/NCA4.2018.CH16 - Solano-Rodríguez, B., Pye, S., Li, P. H., Ekins, P., Manzano, O., & Vogt-Schilb, A. (2021). “Implications of climate targets on oil production and fiscal revenues in Latin America and the Caribbean.” Energy and Climate Change, 2, 100037. - S&P Global (2022), “In the race to diversify and decarbonize, most NOCs remain on the starting blocks,” July 21. - Steele, P., Patel, S., & Volz, U. (2021). Creating a Resilience and Sustainability Trust (RST) at the IMF: How to Support Climate-Vulnerable Countries. - Stern, N., and J. E. Stiglitz (2021), “The Social Cost of Carbon, Risk, Distribution, Market Failures: An Alternative Approach” NBER Working Paper No. 28472. - Svartzman, R., D. Dron, and E. Espagne (2019), From ecological macroeconomics to a theory of endogenous money for a finite planet. Ecological economics, 162, 108-120. - Svartzman, R., and Althouse, J. (2020), “Greening the International Monetary System? Not without Addressing the Political Ecology of Global Imbalances,” Review of International Political Economy. https://doi.org/10.1080/09692290.2020.1854326 - Svartzman, R., E. Espagne, J. Gauthey, P. Hadji-Lazaro, M. Salin, T. Allen, ... and A. Vallier (2021). A “Silent Spring” for the Financial System? Exploring Biodiversity-Related Financial Risks in France. Paris: Banque de France Working Paper No. 826. - Tienhaara, K., R. Thrasher, B. Simmons, and K. Gallagher (2022). “Investor-state disputes threaten the global green energy transition,” Science, 376(6594), 701–703. https://doi.org/10.1126/science.abo4637 - Tiftik, E. and K. Mahmood (2021), “Chipping Away at the Mountain?,” Global Debt Monitor, May 13, Washington, DC: Institute of International Finance. - Tong, D., Q. Zhang, Y. Zheng, K. Caldeira, C. Shearer, C. Hong, Y. Qin, and S. J. Davis (2019), “Committed Emissions from Existing Energy Infrastructure Jeopardize 1.5 °C Climate Target,” Nature 572(7769), 373–377. - Turnheim, B., F. Berkhout, F. Geels, A. Hof, A. McMeekin, B. Nykvist, and D. van Vuuren (2015). Evaluating sustainability transitions pathways: Bridging analytical approaches to address governance challenges. Global environmental change, 35, 239-253. - UNFCCC (2016). Paris Agreement, United Nations Framework Convention on Climate Change, New York City. - Unruh, G. C. (2000), “Understanding Carbon Lock-in,” Energy Policy 28(12), 817–830. - Valdecantos, S. (2023), “The Green Transition Dilemma: The Impossible (?) Quest for Prosperity of South American Economies." AFD Research Papers No. 271. Paris: Agence Française de Développement. - Van de Graaf, T., and M. Bradshaw (2018), “Stranded Wealth: Rethinking the Politics of Oil in an Age of Abundance,” International Affairs 94(6), 1309–1328. - van der Ploeg, F., and A. Rezai (2020), “Stranded assets in the transition to a carbon-free economy,” Annual Review of Resource Economics 12, 281–298. - van Vuuren, D. P., K. I. van der Wijst, S. Marsman, M. van den Berg, A. F. Hof, and C. D. Jones (2020), “The costs of achieving climate targets and the sources of uncertainty.” Nature Climate Change, 10(4), 329-334. - Volz, U., J. Beirne, N. Ambrosio Preudhomme, A. Fenton, E. Mazzacurati, N. Renzhi, and J. Stampe (2020), Climate Change and Sovereign Risk. London, Tokyo, Singapore, and Berkeley, CA: SOAS University of London, Asian Development Bank Institute, World Wide Fund for Nature Singapore, and Four Twenty Seven. - Volz, U. (2022). Climate-proofing the Global Financial Safety Net. Journal of Globalization and Development, 13(1), 1-30. - Wang, S., Z. Hausfather, S. Davis, J. Lloyd, E. Olson, L. Liebermann, L., G. Núñez-Mujica, and J. McBride (2023), “Future demand for electricity generation materials under different climate mitigation scenarios.” Joule, 7(2), 309–332. https://doi.org/https://doi.org/10.1016/j.joule.2023.01.001 - Warnock, F. E., and V. C. Warnock (2009), “International capital flows and US interest rates,” Journal of International Money and Finance, 28(6), 903-919. - Way, R., M. C. Ives, P. Mealy, and J. D. Farmer (2022). “Empirically grounded technology forecasts and the energy transition.” Joule, 6(9), pp.2057-2082. - Weber, I., J. L. Jauregui, L. Teixeira, and L. N. Pires (2022), “Inflation in Times of Overlapping Emergencies: Systemically Significant Prices from an Input-output Perspective,” University of Massachusetts Amherst Working Paper No. 2022-22. - Weber, I., and E. Wasner (2023), “Seller’s Inflation, Profits and Conflict: Why can Large Firms Hike Prices in an Emergency?” Review of Keynesian Economics 11(2), 183-213. - Weiss, M., Junginger, M., Patel, M.K. and Blok, K., 2010. A review of experience curve analyses for energy demand technologies. Technological forecasting and social change, 77(3), pp.411-428. - Welsby, D., J. Price, S. Pye, and P. Ekins (2021), “Unextractable Fossil Fuels in a 1.5°C World,” Nature 597 (July). - Yilmaz, D., B. N. Sawsen, A. Mantes, B. K. Nihed, and I. Daghari (2023). Climate Change, Loss of Agricultural Output and the Macro-Economy: The Case of Tunisia AFD Working Paper No. 452. ### Document identification - Cross-Border Risks of a Global Economy in Mid-Transition - Working Paper No. WP/2023/184 *INTERNATIONAL MONETARY FUND* --- _Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023184-print-pdf.pdf_