Avoiding unreliable climate policy assumptions

🌍 Definitely wonkish, but a must-read for anyone invested in our climate future! Before you dive in, I'd like to explain why this matters. This is about macroeconomic models—tools used to inform policymakers on crucial economic and environmental decisions. 📉💼 Now, all macroeconomic models have their flaws. They're built on assumptions and simplifications that help with forecasting or scenario analysis. 🔍 However, it becomes a major issue when certain assumptions or omissions misrepresent the problem they're analyzing. And that's exactly what's happening with the mainstream models often used for climate change and mitigation scenarios. 🚨🌡️ These models (called Environmental Dynamic Stochastic General Equilibrium (E-DSGE) models) aren’t just abstract concepts; they shape our climate action strategies. 🌎✨ This paper by Yannis Dafermos, Andrew McConnell. Maria Nikolaidi. Servaas Storm and Boyan Yanovski(👉 https://lnkd.in/es7a6ZjM) summarises the critique: 1️⃣ Banks as "Pure Intermediaries" is a Misrepresentation: E-DSGE models view banks merely as pass-through institutions of savings, not as money creators. This ignores banks' ability to create money and underestimates potential macro-financial instability (e.g., green booms or fossil-sector busts) from climate policies. 🌱💸 2️⃣ Demand’s Limited Role in Growth: In E-DSGE models, demand impacts the economy only in the short term. Long-term green investments are seen as costly to GDP since increased green spending must offset other demands. This makes net-zero goals look economically heavier than they might actually be. 📉🌍 3️⃣ Struggle with Disequilibrium & Climate Impact: Rational expectations in E-DSGE models mean agents are assumed to have near-perfect foresight. This framework limits understanding of short-term green investments or rising climate damages, ignoring critical disequilibrium effects in real markets. 📈⚠️ 4️⃣ Unrealistic Substitutability Assumptions: E-DSGE assumes fixed substitutability between fossil and green energy. This limits the capacity of green policies to phase out fossil energy fully and reduces the effectiveness of green monetary policies, even making them appear negligible. 💡🔋 5️⃣Downplaying Fiscal Policy Benefits: E-DSGE suggests green public investments "crowd out" private investments by raising interest rates or requiring higher taxes. This view downplays fiscal policy’s ability to complement private-sector green investments and reduce long-term climate risks. 🏗️🌱 6️⃣ Questionable "Optimal" Policies: E-DSGE models operate in a “second-best” world with inherent market distortions. This makes concepts like “optimal carbon tax” unclear—policies might improve welfare, but not necessarily. 🚫💰 See also a summary of these points below 👇 Better models (and common sense) can help us better than these models.

Mistaking the Alarm for the Fire: A Flaw in Economic Climate Modelling For decades, the climate community has relied on global mean temperature to communicate the effects of climate change. It is a valuable indicator of the planet’s overall energy balance, which drives the climate system. A rising mean temperature serves as our main warning sign that the system is dangerously out of balance. Unfortunately, the distinction between the warning signal and the risk it represents has become blurred, particularly in economic modelling. The Real Risk is Local The warning signal—the increase in global temperature—is not the actual threat to manage. Climate change is a worldwide issue with local impacts. Sea-level rise will vary across different regions; heatwaves will become more severe in some areas, while others may see less heat. To manage the risks of a changing climate, we need reliable models of these localized effects. More importantly, we must understand where these physical hazards intersect with our technological and economic systems—our power grids, supply chains, and population centres. While we have advanced Global Climate Models (GCMs) to forecast physical changes, the economic models that aim to measure the financial impact often fall short. A Critical Flaw in Economic Models Many prominent economic models have a key flaw: they use the warning signal as the mechanism for impact. They wrongly assume that a rise in average temperature directly results in a proportional drop in economic productivity. This method relies on three easily disproved assumptions. 1.    That a change in mean temperature is what stops outdoor work or slows a port, rather than a specific, localized heatwave or cyclone. 2.    The link between warming and economic damage is linear. 3.    The climate remains steady, making past trends a reliable guide for predicting the future. These models estimate the economic impact caused by the loudness of a fire alarm, rather than the fire's location and severity. Modelling the Fire, Not the Alarm To effectively model the economy, we need to look beyond the general signals and include the specific, location-dependent information. The threat to Mexico City is not its average temperature; rather, it is the changing frequency and severity of floods, droughts, and heatwaves that affect its economy. The way forward is to identify the localized effects of the imbalanced climate system—the specific changes in cyclones, wildfires, and extreme precipitation—and map where these events intersect with our economic infrastructure. Only then can we understand the actual impact mechanisms and model the resilience of our financial system. The issue isn’t the fire alarm; it’s the fire. #EconomicModels #fire #climatechange #Impact

Are Climate Model Forecasts Useful for Policy Making? 🫠 Effect of Variable Choice on Reliability and Predictive Validity. For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary. The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from car bon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures. The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The mean correlation between estimation sample statistical fit and forecast accuracy was -0.30. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined. The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even the models validated in this study may fail to improve accuracy relative to naïve forecasts due to uncertainty over the future causal variable values. Our findings emphasise that out-of-sample forecast errors, not statistical fit, should be used to choose between models (hypotheses).    https://lnkd.in/dqZpUgXe