Abstract
The assessment of climate change mitigation policies through economic modeling depends crucially on assumptions under which technological change has been incorporated. Earlier climate-energy-economics modeling attempts heavily relied on exogenous technological change—treating progress as a pure function of time. However, such an approach seems insufficient, especially given developments in endogenous growth theory and innovation economics. A substantial research agenda has emerged to endogenize technological change in large-scale models.
Motivation and Context
The economic analysis of climate change represents one of the most complex challenges in modern resource economics. It is not merely a positive undertaking of prediction but is inextricably linked with normative frameworks that dictate how future welfare is valued and how existential risks are managed.
The assessment of climate policy measures depends fundamentally on three distinct yet interdependent modeling decisions—the discount rate, climate uncertainty, and technological change. This survey focuses on the third pillar: how technological progress is modeled, and why these assumptions fundamentally shape policy recommendations.
Discount Rate
The intertemporal lever determining the weight of future welfare.
Climate Uncertainty
Fat-tailed catastrophic risks may render standard cost-benefit analysis incoherent.
Technological Change
Is innovation exogenous or can policy induce and redirect technological trajectories?
The Dual Market Failure
Climate change and technological progress are closely intertwined through a dichotomy of economic externalities. Pollution represents a negative externality—social costs exceed private costs. Conversely, knowledge and innovation stand as a positive externality—firms cannot fully internalize the returns on R&D investments.
The Architecture of Integrated Assessment
The literature is characterized by a dichotomy between "bottom-up" and "top-down" modeling approaches, each offering distinct advantages and limitations in capturing the nuances of technical progress.
Bottom-up models (MARKAL, MESSAGE, POLES) are rooted in detailed technological description of the energy system. Their strength lies in explicitly modeling learning-by-doing—since specific technologies exist as distinct entities.
Top-down approaches emphasize aggregate economic behavior and include macroeconometric models, computable general equilibrium (CGE) models, and integrated assessment models (IAMs) like DICE, RICE, MERGE, and WITCH.
Model Taxonomy
Exogenous Technical Change
For decades, the standard practice was to treat technological change as exogenous. In these frameworks, energy efficiency improvements are modeled as a pure function of time—essentially as "manna from heaven."
The most common representation is the AEEI parameter (Autonomous Energy Efficiency Improvement)—capturing non-price-driven decline in energy intensity over time. While computationally tractable, this approach implies technological progress is immutable.
Factor-Augmenting Technical Change
Where AD represents the efficiency index for the "dirty" input.
The Cost Divergence
Different assumptions about technological change lead to vastly different policy cost projections. Models with endogenous learning show dramatically lower long-term costs compared to exogenous assumptions.
Endogenous Technical Change
The transition from exogenous to endogenous technological change represents a paradigm shift. This shift acknowledges that innovation is a response to economic incentives—not an autonomous process.
Price-Induced Technical Change: The "induced innovation hypothesis" dates back to Hicks (1932)—changes in relative factor prices induce innovations that economize on the factor that has become relatively more expensive.
Learning-by-Doing (LBD): Arrow's (1962) seminal contribution established that productivity improvements arise as a byproduct of production itself. Wright's Law captures this empirically: costs decline predictably with cumulative production.
The Experience Curve Effect
Directed Technical Change
The most recent theoretical advance comes from Directed Technical Change (DTC)—primarily associated with Daron Acemoglu (2002) and applied to climate by Acemoglu et al. (2012, 2016).
DTC explicitly models the allocation of scientific resources between clean and dirty technologies. Key insights include:
- Path Dependence: Innovation creates its own momentum. If dirty technologies have a productivity advantage, market forces will continue directing R&D toward them.
- Policy Timing: Early intervention can redirect the trajectory permanently. Delay allows dirty technology lock-in to deepen.
- Policy Mix: Carbon taxes alone may be insufficient. The optimal policy combines pricing with technology policy.
Redirecting Innovation
Conclusions & Open Questions
The evolution from exogenous to endogenous technological change in climate-economy models represents a major advance. Yet significant challenges remain:
🔬 Empirical Foundation
Learning rates and R&D elasticities remain uncertain. Bottom-up estimates often exceed top-down results.
⚠️ Crowding-Out Effects
Does green R&D crowd out other innovation? Climate-focused research may reduce productivity growth elsewhere.
🌍 International Spillovers
Knowledge created in one country benefits others, creating free-rider problems in climate R&D.
🎯 Breakthrough Technologies
Current models may underestimate the potential for radical innovation that doesn't follow existing learning curves.
Despite these challenges, the central message is clear: technological change is not manna from heaven. It responds to prices, policies, and expectations. Climate policy is not merely about reducing emissions today—it is about redirecting the entire trajectory of technological development toward a sustainable path.
References
Acemoglu, D. (2002). Directed technical change. Review of Economic Studies, 69(4), 781–809.
Acemoglu, D., et al. (2012). The environment and directed technical change. American Economic Review, 102(1), 131–166.
Arrow, K. J. (1962). The economic implications of learning by doing. Review of Economic Studies, 29(3), 155–173.
Grubb, M., et al. (2002). Induced technical change in energy and environmental modeling. Annual Review of Energy and the Environment, 27, 271–308.
Hicks, J. R. (1932). The Theory of Wages. Macmillan.
Löschel, A. (2002). Technological change in economic models of environmental policy. Ecological Economics, 43(2–3), 105–126.
Nordhaus, W. D. (2007). A review of the Stern Review. Journal of Economic Literature, 45(3), 686–702.
Popp, D. (2002). Induced innovation and energy prices. American Economic Review, 92(1), 160–180.
Stern, N. (2006). The Economics of Climate Change: The Stern Review. Cambridge University Press.
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3(4), 122–128.
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