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Confronting the challenge of integrated assessment of climate adaptation: a conceptual framework

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Abstract

Key limitations of integrated assessment models (IAMs) are their highly stylized and aggregated representation of climate damages and associated economic responses, as well as the omission of specific investments related to climate change adaptation. This paper proposes a framework for modeling climate impacts and adaptation that clarifies the relevant research issues and provides a template for making improvements. We identify five desirable characteristics of an ideal integrated assessment modeling platform, which we elaborate into a conceptual model that distinguishes three different classes of adaptation-related activities. Based on these elements we specify an impacts- and adaptation-centric IAM, whose optimality conditions are used to highlight the types of functional relationships necessary for realistic representations of adaptation-related decisions, the specific mechanisms by which these responses can be incorporated into IAMs, and the ways in which the inclusion of adaptation is likely to affect the simulations’ results.

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Notes

  1. E.g., areas that are currently near the boundaries of different climate regimes will likely experience the largest climatic changes, and impacts, as these regimes shift.

  2. E.g., Landon-Lane et al. (2011) conclude that banking system innovations post-1940 reduced the correlation between climate impacts on agricultural production and financial distress in the U.S. Midwest. Fishback et al. (2011) find access to information to be a significant attenuator of temperature’s influence on mortality, providing support for the beneficial effect of past U.S. public health campaigns. Hansen et al. (2011) find that irrigation and dam infrastructure mitigated the impacts of precipitation extremes on agricultural yields.

  3. The state of the art in this regard is the FUND model’s “impact sectors” (Tol 1995; Anthoff et al. 2011), but this approach tends to conflate biophysical endpoints with the parts of the economy they might affect. The concern is that some impacts might induce substitution of output from more-exposed to less-exposed industries.

  4. Including labor would create the equivalent of an additional “fixed factor” in eq. (1c), which would not generate additional insight. We note that our existing formulation is nonetheless able to accommodate in an implicit fashion the potential output effects of climate impacts on labor productivity (e.g., Graff Zivin and Neidell 2010).

  5. Compare the special case of DICE/RICE-type IAMs in which damages have a neutral effect, which in the current notation can be specified as \( q_{j,l,t}^Y={\Lambda_{j,l,t }}\cdot {\psi_{j,l }}\left[ {q_{j,l,t}^E,q_{j,l,t}^K} \right] \).

  6. These could potentially be captured through modifications to the sectoral output aggregation functions (1b) or the perpetual inventory equations (1h).

  7. Eq. (1j) can be used to substitute for G in (1k), and the outcome can be plugged into (1l) for M m. The new eq. (1l) can be plugged into (1m) to eliminate b i. The result, which expresses regional and sectoral climate shocks as a function of lagged values of carbon-energy, as well as current investment in reactive adaptation and the extant stock of proactive adaptation capacity, can be used to eliminate Λ in (1c). Eq. (1c) can be further reduced by using (1d) and (1e) to express inputs of carbon-energy and capital to sectoral production as implicit functions of their aggregate supplies, Q K and Q E. And (1h) and (1i) can be used to express the current state variables Q K and a as functions of the lagged values of their respective controls, X K and π. The new eq. (1c) can be used to substitute for q Y in (1b) and the result plugged into (1f) to eliminate Q Y. Finally, (1g) can be substituted into (1f) to eliminate P E. The new eq. (1f) can then be used to substitute for Q C in (1a), allowing our IAM to be approximated as an unconstrained maximization problem in which the objective is denominated over the four control variables:

    $$ \mathcal{W}={{\mathbb{E}}_0}\mathop{\sum}\limits_{t=0}^T{\beta^t}\varXi \left[ {\ldots, {U_{{{\ell^{\prime }}}}}\left[ {X_{{{\ell^{\prime }},t-1}}^K,X_{{{\ell^{\prime }},t-2}}^K,\ldots,Q_{{{\ell^{\prime }},t}}^E,Q_{{{\ell^{\prime }},t-1}}^E,\ldots,{\rho_{{j,{\ell^{\prime }},t}}},{\pi_{{j,{\ell^{\prime }},t-1}}},{\pi_{{j,{\ell^{\prime }},t-2}}},\ldots } \right],\ldots } \right]. $$
  8. The marginal effect of investment at t′ on subsequent capital stocks is \( {{\left( {1-{\vartheta^K}} \right)}^{{t-{t^{\prime }}-1}}} = {\gamma_4}\left[ {Q_{{\ell, t}}^K,X_{{\ell, {t^{\prime}}}}^K} \right]\cdot {{{Q_{{\ell, t}}^K}} \left/ {{X_{{\ell, {t^{\prime}}}}^K}} \right.} \).

  9. Obviously, there is also uncertainty associated with the distribution of future output growth across sectors and regions (gY), but this is comparatively straightforward to address through Monte Carlo simulation, even in existing IAMs.

  10. Interestingly, while a long-run carbon energy extraction cost function along the lines of (1d) were incorporated into early versions of RICE (Nordhaus and Boyer 2001: eq. (2.12)) this feature is absent from recent variants of the model (Nordhaus 2008, 2010), in all likelihood because the particular parameterization of extraction cost was inelastic over a broad range (γ 7→0 even for large t, cf Nordhaus and Boyer 2001: Fig. 3.4).

  11. Similar to footnote 5, \( {{\left( {1-{\vartheta^i}} \right)}^{{\left( {t-{t^{\prime }}-1} \right)}}}={\gamma_6}\left[ {a_{{{j^{\prime }},{\ell^{\prime}}}}^i,\pi_{{{j^{\prime }},{\ell^{\prime}}}}^i} \right]{{{a_{{{j^{\prime }},{\ell^{\prime}}}}^i}} \left/ {{\pi_{{{j^{\prime }},{\ell^{\prime}}}}^i}} \right.} \).

  12. It is possible to substitute the optimal values of our controls into the capacity constraint (1f) to solve for the level of period-t′ consumption, and, with (1a), welfare. However, even the single-period algebraic result is complicated enough to defy easy interpretation.

  13. See Fisher-Vanden et al. (2012).

  14. Recall that for non-independent random variables A and B, \( \mathbb{E}\left[ {AB} \right]=\mathbb{E}\left[ A \right]\mathbb{E}\left[ B \right]+Co\mathrm{v}[AB] \).

  15. See Fisher-Vanden et al. (2012).

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Correspondence to Karen Fisher-Vanden.

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This article is part of a Special Issue on “Improving the Assessment and Valuation of Climate Change Impacts for Policy and Regulatory Analysis” edited by Alex L. Marten, Kate C. Shouse, and Robert E. Kopp.

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Wing, I.S., Fisher-Vanden, K. Confronting the challenge of integrated assessment of climate adaptation: a conceptual framework. Climatic Change 117, 497–514 (2013). https://doi.org/10.1007/s10584-012-0651-x

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