Papers
Topics
Authors
Recent
2000 character limit reached

A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty (2310.13200v1)

Published 19 Oct 2023 in econ.GN, cs.LG, and q-fin.EC

Abstract: We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R&D investment and leads to technological innovation in green sector productivity. To solve our high-dimensional, non-linear model framework we implement a neural-network-based global solution method. We show there are first-order impacts of model uncertainty on optimal decisions and social valuations in our integrated climate-economic-innovation framework. Accounting for interconnected uncertainty over climate dynamics, economic damages from climate change, and the arrival of a green technological change leads to substantial adjustments to investment in the different capital types in anticipation of technological change and the revelation of climate damage severity.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (103)
  1. The environment and directed technical change. American economic review 102 (1):131–66.
  2. Transition to clean technology. Journal of Political Economy 124 (1):52–104.
  3. Extensions of the deep Galerkin method. Applied Mathematics and Computation 430:127287.
  4. Technical Summary: Global warming of 1.5∘\,{}^{\circ}start_FLOATSUPERSCRIPT ∘ end_FLOATSUPERSCRIPTC. An IPCC Special Report on the impacts of global warming of 1.5∘\,{}^{\circ}start_FLOATSUPERSCRIPT ∘ end_FLOATSUPERSCRIPTC above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Tech. rep., IPCC.
  5. A quartet of semigroups for model specification, robustness, prices of risk, and model detection. Journal of the European Economic Association 1 (1):68–123.
  6. Arrhenius, Svante. 1896. On the influence of Carbonic Acid in the Air upon Temperatue of the Ground. Philosophical Magazine and Journal of Science Series 5 41:237–276.
  7. Deep equilibrium nets. International Economic Review 63 (4):1471–1525.
  8. Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications. Methodology and Computing in Applied Probability 1–36.
  9. Barnett, Michael. 2023. Climate change and uncertainty: An asset pricing perspective. Management Science .
  10. Pricing Uncertainty Induced by Climate Change. Review of Financial Studies 33 (3):1024–1066.
  11. Climate Change Uncertainty Spillover in the Macroeconomy. Prepared for the 2021 Macoreconomics Annual .
  12. How Should Climate Change Uncertainty Impact Social Valuation and Policy? Working Paper .
  13. Baumol, William J. 1986. Productivity growth, convergence, and welfare: what the long-run data show. The american economic review 1072–1085.
  14. Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. Journal of Nonlinear Science 29 (4):1563–1619.
  15. Deep splitting method for parabolic PDEs. SIAM Journal on Scientific Computing 43 (5):A3135–A3154.
  16. A toolkit of policies to promote innovation. Journal of economic perspectives 33 (3):163–84.
  17. Net zero by 2050: A roadmap for the global energy sector. Tech. rep., International Energy Agency.
  18. Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. arXiv preprint arXiv:2205.03672 .
  19. Brock, William A. 1982. Asset prices in a production economy. In The economics of information and uncertainty, 1–46. University of Chicago Press.
  20. Brock, William A and Leonard J Mirman. 1972. Optimal economic growth and uncertainty: the discounted case. Journal of Economic Theory 4 (3):479–513.
  21. Does the terrestrial biosphere have planetary tipping points? Trends in Ecology and Evolution 28:396–401.
  22. Cai, Yongyang and Thomas S Lontzek. 2019. The social cost of carbon with economic and climate risks. Journal of Political Economy 127 (6):2684–2734.
  23. Environmental Tipping Points Significantly Affect the Cost-Benefit Assessment of Climate Policies. Proceedings of the National Academy of Sciences 112 (15):4606–4611.
  24. The Social Cost of Carbon with Climate Risk. Tech. rep., Hoover Institution, Stanford, CA.
  25. Solving the quantum many-body problem with artificial neural networks. Science 355 (6325):602–606.
  26. Carleton, Tamma and Michael Greenstone. 2021. Updating the United States Government’s Social Cost of Carbon. SSRN Working Paper 2021-04, University of Chicago, Becker Friedman Institute for Economics.
  27. Carmona, René and Mathieu Laurière. 2022. Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: II—the finite horizon case. The Annals of Applied Probability 32 (6):4065–4105.
  28. Castro, Javier. 2022. Deep learning schemes for parabolic nonlocal integro-differential equations. Partial Differential Equations and Applications 3 (6):77.
  29. Making Decisions under Model Misspecification.
  30. Cochrane, John H. 1991. Production-based asset pricing and the link between stock returns and economic fluctuations. The Journal of Finance 46 (1):209–237.
  31. Temperature and growth: A panel analysis of the United States. Journal of Money, Credit and Banking 51 (2-3):313–368.
  32. Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal: Macroeconomics 4 (3):66–95.
  33. Dietz, Simon and Frank Venmans. 2019. Cumulative carbon emissions and economic policy: In search of general principles. Journal of Environmental Economics and Management 96:108–129.
  34. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proceedings of the National Academy of Sciences 112 (43):E5777–E5786.
  35. Duarte, Victor. 2018. Machine learning for continuous-time finance. Tech. rep., Tech. rep., Gies College of Business.
  36. Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Commun. Math. Stat. 5 (4):349–380.
  37. Eberly, Janice and Neng Wang. 2009. Reallocating and pricing illiquid capital. In American Economic Review, Papers and Proceedings.
  38. Lifetime of anthropogenic climate change: Millennial time scales of potential CO2 and surface temperature perturbations. Journal of Climate 22 (10):2501–2511.
  39. Solving high-dimensional dynamic programming problems using deep learning. Unpublished working paper .
  40. Emissions Are Still Rising: Ramp Up the Cuts. Nature 564:27–30.
  41. Friedlingstein, Pierre. 2015. Carbon cycle feedbacks and future climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373 (20140421).
  42. Gao, X. and L.-M. Duan. 2017. Efficient representation of quantum many-body states with deep neural networks. Nature Communications 8 (1):662.
  43. Transient Climate Response in a Two-Layer Energy-Balance Model. Part {I}: Analytical Solution and Parameter Calibration Using CMIP5 AOGCM Experiments. Journal of Climate 26 (6):1841–1857.
  44. A deep solver for BSDEs with jumps. arXiv preprint arXiv:2211.04349 .
  45. Optimal Taxes on Fossil Fuel in General Equilibrium. Econometrica 82 (1):41–88.
  46. Monotonic calibrated interpolated look-up tables. The Journal of Machine Learning Research 17 (1):3790–3836.
  47. Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences 115 (34):8505–8510.
  48. Uniformly accurate machine learning-based hydrodynamic models for kinetic equations. Proceedings of the National Academy of Sciences 116 (44):21983–21991.
  49. Han, Jiequn and Weinan E. 2016. Deep Learning Approximation for Stochastic Control Problems. Deep Reinforcement Learning Workshop, NIPS, arXiv preprint arXiv:1611.07422 .
  50. Han, Jiequn and Ruimeng Hu. 2021. Recurrent neural networks for stochastic control problems with delay. Mathematics of Control, Signals, and Systems 33 (4):775–795.
  51. Han, Jiequn and Jihao Long. 2020. Convergence of the deep BSDE method for coupled FBSDEs. Probability, Uncertainty and Quantitative Risk 5:1–33.
  52. Hansen, Lars Peter and Jianjun Miao. 2018. Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments. Proceedings of the National Academy of Sciences 115 (37):9163–9168.
  53. Hansen, Lars Peter and Thomas J. Sargent. 2007. Recursive Robust Estimation and Control without Commitment. Journal of Economic Theory 136 (1):1–27.
  54. The Consequences of Uncertainty: Climate Sensitivity and Economic Sensitivity to the Climate. Annual Review of Economics 10:189–205.
  55. Estimating Economic Damage from Climate Change in the United States. Science 356 (6345):1362–1369.
  56. Deep backward schemes for high-dimensional nonlinear PDEs. Mathematics of Computation 89 (324):1547–1579.
  57. On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations. Journal of Scientific Computing 79 (3):1534–1571.
  58. Jaakkola, Niko and Frederick Van der Ploeg. 2019. Non-cooperative and cooperative climate policies with anticipated breakthrough technology. Journal of Environmental Economics and Management 97:42–66.
  59. Mission net-zero America: The nation-building path to a prosperous, net-zero emissions economy.
  60. Jermann, Urban J. 1998. Asset pricing in production economies. Journal of monetary Economics 41 (2):257–275.
  61. Three algorithms for solving high-dimensional fully coupled FBSDEs through deep learning. IEEE Intelligent Systems 35 (3):71–84.
  62. Carbon Dioxide and Climate Impulse Response Functions for the Computation of Greenhouse Gas Metrics: A Multi-Model Analysis. Atmospheric Chemistry and Physics 13 (5):2793–2825.
  63. Kingma, Diederik P and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
  64. Recursive {Smooth} {Ambiguity} {Preferences}. Journal of Economic Theory 144:930–976.
  65. Kogan, Leonid and Dimitris Papanikolaou. 2014. Growth opportunities, technology shocks, and asset prices. The journal of finance 69 (2):675–718.
  66. Kung, Howard and Lukas Schmid. 2015. Innovation, growth, and asset prices. The Journal of Finance 70 (3):1001–1037.
  67. Lemoine, Derek and Christian Traeger. 2014. Watch your step: optimal policy in a tipping climate. American Economic Journal: Economic Policy 6 (1):137–66.
  68. Tipping Elements in the Earth’s Climate System. Proceedings of the National Academy of Sciences 105 (6):1786–1793.
  69. Levitan, David. 2013. Quick-Change Planet: Do Global Climate Tipping Points Exist? Scientific American .
  70. Lucas Jr, Robert E. 1988. On the mechanics of economic development. Journal of monetary economics 22 (1):3–42.
  71. Have R&D spillovers declined in the 21st century? Fiscal Studies 40 (4):561–590.
  72. Dynamic Variational Preferences. Journal of Economic Theory 128 (1):4–44.
  73. The Uncertainty in the Transient Climate Response to Cumulative CO22{}_{2}start_FLOATSUBSCRIPT 2 end_FLOATSUBSCRIPT Emissions Arising from the Uncertainty in Physical Climate Parameters. Journal of Climate 30 (2):813–827.
  74. Deep learning for solving dynamic economic models. Journal of Monetary Economics 122:76–101.
  75. Summary for Policymakers - Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Book section, IPCC, Cambridge, United Kingdom and New York, NY, USA.
  76. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Tech. rep., IPCC.
  77. The proportionality of global warming to cumulative carbon emissions. Nature 459 (7248):829–832.
  78. McKinsey Global Initiative. 2022. The net-zero transition: What it would cost, what it could bring. Mckensey Global Institute.
  79. Nordhaus, William. 2018. Projections and uncertainties about climate change in an era of minimal climate policies. American Economic Journal: Economic Policy 10 (3):333–360.
  80. ———. 2019. Economics of the disintegration of the Greenland ice sheet. Proceedings of the National Academy of Sciences 116 (25):12261–12269.
  81. OECD. 2019. Regions in industrial transition: Policies for people and places. ORGANIZATION FOR ECONOMIC.
  82. A Climate Sensitivity Estimate using Bayesian Fusion of Instrumental Observations and an Earth System Model. Journal of Geophysical Research Atmospheres 117 (D04103):1–11.
  83. Palmer, Tim and Bjorn Stevens. 2019. The scientific challenge of understanding and estimating climate change. Proceedings of the National Academy of Sciences 116 (49):24390–24395.
  84. Papanikolaou, Dimitris. 2011. Investment shocks and asset prices. Journal of Political Economy 119 (4):639–685.
  85. Neural networks-based backward scheme for fully nonlinear PDEs. SN Partial Differential Equations and Applications 2 (1):16.
  86. Pierrehumbert, Ramond T. 2014. Short-Lived Climate Pollution. Annual Review of Earth and Planetary Science 42:341–379.
  87. Summary for Policymakers - Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Book section, IPCC, Cambridge, UK. In Press.
  88. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378:686–707.
  89. Ricke, Katharine L. and Ken Caldeira. 2014. Maximum Warming Occurs about One Decade After a Carbon Dioxide Emission. Environmental Research Letters 9 (12):1–8.
  90. Overshooting tipping point thresholds in a changing climate. Nature 592 (7855):517–523.
  91. Romer, Paul M. 1990. Endogenous technological change. Journal of political Economy 98 (5, Part 2):S71–S102.
  92. Rudik, Ivan. 2020. Optimal Climate Policy When Damages Are Unknown. American Economic Journal: Economic Policy 12 (2):340–73.
  93. Runje, Davor and Sharath M Shankaranarayana. 2022. Constrained Monotonic Neural Networks. arXiv preprint arXiv:2205.11775 .
  94. Saporito, Yuri F and Zhaoyu Zhang. 2021. Path-dependent deep Galerkin method: A neural network approach to solve path-dependent partial differential equations. SIAM Journal on Financial Mathematics 12 (3):912–940.
  95. Sauzet, Maxime. 2021. Projection methods via neural networks for continuous-time models. Available at SSRN 3981838 .
  96. Sill, Joseph. 1997. Monotonic networks. Advances in neural information processing systems 10.
  97. DGM: A deep learning algorithm for solving partial differential equations. Journal of computational physics 375:1339–1364.
  98. Stine, Deborah D. 2008. The Manhattan Project, the Apollo program, and federal energy technology R & D programs: A comparative analysis. Library of Congress.
  99. Empirically grounded technology forecasts and the energy transition. Joule 6 (9):2057–2082.
  100. Wehenkel, Antoine and Gilles Louppe. 2019. Unconstrained monotonic neural networks. Advances in neural information processing systems 32.
  101. Weitzman, Martin L. 2012. GHG Targets as Insurance Against Catastrophic Climate Damages. Journal of Public Economic Theory 14 (2):221–244.
  102. White House. 2021. The long-term strategy of the United States: pathways to net-zero greenhouse gas emissions by 2050.
  103. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Physical Review Letters 120 (14):143001.
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now

Whiteboard

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 7 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free