Anthoulla Phella, Vasco J. Gabriel, Luis F. Martins
arXiv 11 Sep 2025 · Econometrics · publishedInternational Journal of Forecasting (2026)
arXiv:2509.09384 · PDF · DOI · OpenAlex · Extracted main text
In this paper, using the Bayesian VAR framework suggested by Chan et al. (2025), we produce conditional temperature forecasts up until 2050, by exploiting both equality and inequality constraints on climate drivers like carbon dioxide or methane emissions. Engaging in a counterfactual scenario analysis by imposing a Shared Socioeconomic Pathways (SSPs) scenario of "business as-usual", with no mitigation and high emissions, we observe that conditional and unconditional forecasts would follow a similar path. Instead, if a high mitigation with low emissions scenario were to be followed, the conditional temperature paths would remain below the unconditional trajectory after 2040, i.e. temperatures increases can potentially slow down in a meaningful way, but the lags for changes in emissions to have an effect are quite substantial. The latter should be taken into account greatly when designing response policies to climate change.
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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 | Chan, Joshua C.C., Pettenuzzo, Davide, Poon, Aubrey, & Zhu, Dan (2025) Conditional forecasts in large Bayesian VARs with multiple equality and inequality constraints | 1.000 | 6 | 3 | 100% |
| 2 | Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel… (2020) The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500 | 0.693 | 7 | 1 | 100% |
| 3 | Chan, Joshua CC, Poon, Aubrey, & Zhu, Dan (2023) High-dimensional conditionally Gaussian state space models with missing data | 0.644 | 2 | 2 | 100% |
| 4 | Waggoner, Daniel F, & Zha, Tao (1999) Conditional forecasts in dynamic multivariate models | 0.644 | 2 | 2 | 100% |
| 5 | Antolín-Díaz, J., Petrella, I., & Rubio-Ramírez, J.F (2021) Structural scenario analysis with SVARs | 0.511 | 2 | 1 | 100% |
| 6 | Chan, J. C. C., & Jeliazkov, I (2009) Efficient simulation and integrated likelihood estimation in state space models | 0.511 | 2 | 1 | 100% |
| 7 | Stock, James H, & Watson, Mark W (1998) Diffusion Indexes | 0.511 | 2 | 1 | 100% |
| 8 | Baumeister, C., & Kilian, L (2014) Real-time analysis of oil price risks using forecast scenarios | 0.405 | 1 | 1 | 100% |
| 9 | Hendry, David F., & Pretis, Felix (2023) Analysing differences between scenarios | 0.405 | 1 | 1 | 100% |
| 10 | Hasselmann, K (1993) Optimal Fingerprints for the Detection of Time-dependent Climate Change | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.