Destructive climate-induced extreme events increasingly affect people and economies worldwide. Their impacts are widely studied using both empirical and simulation methods. Yet, the scientific debate on whether environmental shocks induce growth spurts, leave persistent scars on the economy, or barely have any long-term effects, remains unresolved. Here, we show how differences in aggregate economic dynamics can be explained by heterogeneity at the firm-level, specifically the distribution of damages among firms and different productivity level of affected firms. We employ a novel multi-regional economic agent-based model, where firms in one of the regions are struck by a climate-induced shock. We find that these firm-level heterogeneities have significant effects on aggregate economic dynamics, with long-run outcomes ranging from full recovery to modest growth, and even to persistent depression. Our results show that shocks to clusters of economic activity can have outsized impacts on regional economies compared to a representative distribution of impacts. This highlights fundamental problems with conventional aggregated analysis of physical climate risks and of overall costs of climate change, suggesting that policy-focused analysis could be misguided when omitting a granular representation of economic agents.

We combine the Environmentally-Extended Multi-Regional Input-Output (EE MRIO) analysis with a microsimulation analysis to estimate the distributional implications of carbon policy reform, a combination of carbon tax and revenue recycling initiatives, on households in Indonesia. We consider two relevant scenarios: an “economy-wide” carbon tax versus an “electricity-only” carbon tax. The impact of carbon policy reform is measured by the net impact of carbon tax and cash transfer relative to initial expenditure. Carbon policy reform in Indonesia tends to be progressive, meaning the relative net impact on households decreases as income increases. Carbon tax in Indonesia primarily affects households through the price increase in electricity and fuel products. The distributional impacts of a carbon policy reform are determined more by the percentage of tax revenue recycled and taxation scenario and less by the tax rate. In order to protect the poorest 40 % of Indonesian households from inflationary pressure, the Indonesian government needs to recycle 25 % of tax revenue.

Climate-induced hazards are becoming more frequent and severe, causing escalating economic losses worldwide. Consequently, climate change adaptation is increasingly necessary to protect people, nature and the economy. However, little is known about who is adapting and how much they spend on adaptation measures, especially in the private sector. This article focuses on firms—the backbone of economic development, yet understudied in climate adaptation research. Here we present insights from a unique panel dataset detailing businesses’ adaptation investments across 28 European countries (2018–2022), 5 hazard types, and 19 economic sectors. Our descriptive analysis reveals low but increasing adaptation investments across Europe (0.15–0.92% of national gross domestic product, annually increasing by 30.6–37.4%). Moreover, we highlight considerable differences in adaptation intensity across sectors, including low adaptation intensity in manufacturing and retail trade. Additionally, our econometric analysis indicates that public adaptation spending crowds in private investments in adaptation, highlighting opportunities to facilitate autonomous adaptation.

The AI industry is betting that ‘scaling’, i.e., adding more and more data, GPUs, compute infrastructure and dollars, will lead to machine superintelligence or Artificial General Intelligence (AGI)—which in turn will lead to exponential growth of output, productivity and profits for the industry and the larger American economy. Focusing on AGI and generic LLMs, the point of this article is plain: AI’s ‘scaling’ strategy must fail and the AI data-center investment bubble will pop. The article identifies four bottlenecks: (1) the planned $5 trillion investment in data center infrastructure (during 2026–2030) is not going to pay off; AI revenues will not increase enough and AI inference cost continue to rise faster than revenues; (2) AI firms will have to resort to hyper-scale borrowing from banks and investment-grade bond markets to fund their capex; this hyperscale borrowing will create a ticking time bomb on the balance sheets of AI firms, because the core capital expenditure on specialized GPUs and server risks becoming economically obsolete within two or three years; (3) it will be impossible to build the projected data center infrastructure fast enough, because upstream suppliers—producing everything from copper wire to turbines to transformers and switchgear—will run into labor shortages, long waiting times for power grid connections, material bottlenecks and regulatory blowback; and (4) the strategic bet of frontier AI firms that AGI can be achieved by building ever more data centers and using ever more chips is already going bad; AI products will continue to be untrustworthy for high-stake usage. As a result, the magical projections of exponential growth, which defy economic and financial logic and fatally ignore unforgiving real-world constraints will turn out to be wrong. The fact that the AI industry is the main source of growth in an otherwise sclerotic U.S. economy and is driven by a concentrated set of hyper-scalers engaging in ‘circular’ financial transactions based on aggressively optimistic long-term cash flow-generating potential should be a very serious cause for concern.

Bernanke and Blanchard use a simple dynamic New Keynesian model of wage-price determination to explain the sharp acceleration in U.S. inflation during 2021–2023. They claim that their model closely tracks the pandemic-era inflation and they confidently conclude that “… we don’t think that the recent experience justifies throwing out existing models of wage-price dynamics.” This paper argues that this confidence is misplaced. The Bernanke and Blanchard is another failed attempt to salvage establishment macroeconomics after the massive onslaught of adverse inflationary circumstances with which it could evidently not contend. It misrepresents American economic reality, hides distributional issues from view, de-politicizes (monetary and fiscal) policy-making, and sets monetary policymakers up to deliver significantly more monetary tightening than can be justified on the basis of more realistic model analyses.

Economic costs of climate change are conventionally assessed at the aggregated global and national levels, while adaptation is local. When present, regionalised assessments are confined to direct damages, hindered by both data and models’ limitations. This article goes beyond the aggregated analysis to explore direct and indirect economic consequences of sea level rise (SLR) at regional and sectoral levels in Europe. Using a dynamic computable general equilibrium model and novel datasets, we estimate the distribution of losses and gains across regions and sectors. A comparison of a high-end scenario against a no-climate-impact baseline suggests a GDP loss of 1.26% (€871.8 billion) for the whole EU&UK. Conversely our refined assessments show that some coastal regions lose 9.56–20.84% of GDP, revealing striking regional disparities. Inland regions grow due to the displaced demand from coastal areas, but the GDP gains are small (0–1.13%). While recovery benefits the construction sector, public services and industry face significant downturns. We show that prioritising recovery of critical sectors locally reduces massive regional GDP losses, at negligible costs to the overall European economy. Our analysis traces regional economic restructuring triggered by SLR, underscoring the necessity of region-specific adaptation policies that embrace uneven geographic impacts and unique sectoral profiles to inform resilient strategy design.

The macroeconomic models used by major institutions including the Federal Reserve and the International Monetary Fund (IMF) failed to predict the inflation surge during 2021–2023. The output gap, the unemployment gap, the New Keynesian Phillips curve and inflation expectations did not give timely and relevant signals. The re-emergence of inflation thus threw the ‘science of monetary policy’ off the rails. Faced with the choice between changing their paradigm and proving that there is no need to do so, the ‘scientists of monetary policy’ got busy on the proof. As a result, a number of ad hoc epicycles have been added to the New Keynesian analytical core – with the help of which one can claim to be able to explain the sudden acceleration of inflation post factum. This paper critically reviews the theoretical and empirical merits of three recent tweaks to the New Keynesian core: using the vacancy ratio as the appropriate measure of real economic activity; hammering on the considerable risk of an imminent wage–price spiral; and the resurrection of the non-linear Phillips curve. The paper concludes by drawing out sobering lessons concerning the art of paradigm maintenance as practiced by the ‘scientists of monetary policy’.