Guillaume Chevillon, Takamitsu Kurita
arXiv 11 Jul 2023 · Econometrics
arXiv:2307.05818 · PDF · DOI · OpenAlex · Extracted main text
This paper tests the feasibility and estimates the cost of climate control through economic policies. It provides a toolbox for a statistical historical assessment of a Stochastic Integrated Model of Climate and the Economy, and its use in (possibly counterfactual) policy analysis. Recognizing that stabilization requires supressing a trend, we use an integrated-cointegrated Vector Autoregressive Model estimated using a newly compiled dataset ranging between years A.D. 1000-2008, extending previous results on Control Theory in nonstationary systems. We test statistically whether, and quantify to what extent, carbon abatement policies can effectively stabilize or reduce global temperatures. Our formal test of policy feasibility shows that carbon abatement can have a significant long run impact and policies can render temperatures stationary around a chosen long run mean. In a counterfactual empirical illustration of the possibilities of our modeling strategy, we study a retrospective policy aiming to keep global temperatures close to their 1900 historical level. Achieving this via carbon abatement may cost about 75% of the observed 2008 level of world GDP, a cost equivalent to reverting to levels of output historically observed in the mid 1960s. By contrast, investment in carbon neutral technology could achieve the policy objective and be self-sustainable as long as it costs less than 50% of 2008 global GDP and 75% of consumption.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ikefuji, M., R. J. Laeven, J. R. Magnus, and C. Muris (2020) Expected utility and catastrophic risk in a stochastic economy–climate model | 0.511 | 2 | 1 | 100% |
| 2 | Barnett, M., W. Brock, and L. P. Hansen (2022) Climate change uncertainty spillover in the macroeconomy | 0.405 | 1 | 1 | 100% |
| 3 | Castle, J. L., D. F. Hendry, and A. B. Martinez (2017) Evaluating forecasts, narratives and policy using a test of invariance | 0.405 | 1 | 1 | 100% |
| 4 | Hänsel, M. C., M. D. Bauer, M. A. Drupp, G. Wagner, and G. D. Rudebu… (2022) Climate policy curves: Linking policy choices to climate outcomes | 0.405 | 1 | 1 | 100% |
| 5 | Chahrour, R. and K. Jurado (2022) Recoverability and expectations-driven fluctuations | 0.405 | 1 | 1 | 100% |
| 6 | Chang, Y., R. K. Kaufmann, C. S. Kim, J. I. Miller, J. Y. Park, and… (2020) Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate | 0.405 | 1 | 1 | 100% |
| 7 | Diebold, F. X., M. Goebel, and P. Goulet Coulombe (2023) Assessing and comparing fixed-target forecasts of arctic sea ice: Glide charts for feature-engineered linear regression and mach… | 0.405 | 1 | 1 | 100% |
| 8 | Duffy, J. A. and D. F. Hendry (2017) The impact of integrated measurement errors on modeling long-run macroeconomic time series | 0.405 | 1 | 1 | 100% |
| 9 | Hassler, J., P. Krusell, and C. Olovsson (2018) The consequences of uncertainty: Climate sensitivity and economic sensitivity to the climate | 0.405 | 1 | 1 | 100% |
| 10 | Johansen, S. and K. Juselius (2001) Controlling inflation in a cointegrated vector autoregressive model with an application to US data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.