The Economic Impacts of Climate Change

Tol, Richard S.J.

doi:10.1093/reep/rex027

Cited by 569 publications

(314 citation statements)

References 132 publications

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“…For our study's dataset, we combine the studies included in the most recent analyses by Tol (2014Tol ( , 2015b 15 and Newbold and Marten (2014). We double-check estimates-including re-aggregating damage estimates using GDP weights (see Appendix A1)-and correct errors when present (see Appendix A2).…”

Section: Datamentioning

confidence: 99%

“…We are interested solely in global willingness to pay to avoid climate change, and drop all studies that measure impacts in terms of compensating surplus (CS): Maddison (2003), Rehdanz and Maddison (2005), and Maddison and Rehdanz (2011); we conduct sensitivity analysis to this assumption in Appendix C in the Supplementary Material. 20 In the case of Meyer and Cooper (1995), who apply the value of statistical life from OECD nations and use 15 Of the points in Tol (2014Tol ( , 2015b, we automatically exclude Nordhaus (2013) from consideration because it is a product of a meta-analysis using the studies in Tol (2009); these are a subset of the data cited in Tol (2014Tol ( , 2015b. 16 The search terms were: "percentage of GDP", "climate change", & global; "percent of GDP", "climate change", & global; "percent GDP", "climate change", & global; "% of GDP", "climate change", & global; "% GDP", "climate change", & global; "climate change" & "world output"; "Estimated impact of global warming on world output"; "climate change", "economic impact", & global; "climate change" & "global impact".…”

Section: Datamentioning

confidence: 99%

“…We use the Nordhaus (2013) damage functional form in our main text, despite some alternative damage functional forms (Weitzman 2012;Tol 2014Tol , 2015b) that fit the data slightly better. These alternative functional forms increase the number of explanatory variables, running the risk of overfitting the model.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Howard

Sterner

2017

Environ Resource Econ

208

100

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Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4 • C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature-damage relationship, we find a three-to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four-to five-fold.

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Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Howard

Sterner

2017

Environ Resource Econ

208

100

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“…A rapidly growing literature investigates the relationship between climate (temperature, precipitation, storms, and other aspects of the weather) and economic performance (agricultural production, labor productivity, commodity prices, health, con ‡ict, and economic growth)-see the recent surveys by Dell et al (2014) and Tol (2009). This is important as a careful understanding of the climate-economy relationship is essential to the e¤ective design of appropriate institutions and macroeconomic policies, as well as enabling forecasts of how future changes in climate will a¤ect economic activity.…”

Section: Introductionmentioning

confidence: 99%

Fair weather or foul? The macroeconomic effects of El Niño

Cashin

Mohaddes

Raissi

2017

Journal of International Economics

185

118

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This paper employs a dynamic multi-country framework to analyze the international macroeconomic transmission of El Niño weather shocks. This framework comprises 21 country/region-specific models, estimated over the period 1979Q2 to 2013Q1, and accounts for not only direct exposures of countries to El Niño shocks but also indirect effects through third-markets. We contribute to the climate-macroeconomy literature by exploiting exogenous variation in El Niño weather events over time, and their impact on different regions cross-sectionally, to causatively identify the effects of El Niño shocks on growth, inflation, energy and non-fuel commodity prices. The results show that there are considerable heterogeneities in the responses of different countries to El Niño shocks. While Australia, Chile, Indonesia, India, Japan, New Zealand and South Africa face a short-lived fall in economic activity in response to an El Niño shock, for other countries (including the United States and European region), an El Niño occurrence has a growth-enhancing effect. Furthermore, most countries in our sample experience short-run inflationary pressures as both energy and non-fuel commodity prices increase. Given these findings, macroeconomic policy formulation should take into consideration the likelihood and effects of El Niño weather episodes.

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“…This design was used widely in early econometric analyses of the e↵ect of the climate (Fankhauser, 1995;Tol, 2009), gaining prominence in the seminal work by Mendelsohn et al (1994) who regressed farm prices across US counties on growing season temperatures and observable characteristics of farm properties. This implementation highlights a major strength of this approach in the context of climatic e↵ects: since farmers who inhabit a location for a long period will have a strong grasp of C at their location and will adjust farm investments and management to optimize based on these beliefs, farm prices can be assumed to reflect all direct e↵ects as well as all belief…”

Section: Cross-sectional Approachesmentioning

confidence: 99%

Climate Econometrics

Hsiang¹

2016

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Identifying the effect of climate on societies is central to understanding historical economic development, designing modern policies that react to climatic events, and managing future global climate change. Here, I review, synthesize, and interpret recent advances in methods used to measure effects of climate on social and economic outcomes. Because weather variation plays a large role in recent progress, I formalize the relationship between climate and weather from an econometric perspective and discuss their use as identifying variation, highlighting tradeoffs between key assumptions in different research designs and deriving conditions when weather variation exactly identifies the effects of climate. I then describe advances in recent years, such as parameterization of climate variables from a social perspective, nonlinear models with spatial and temporal displacement, characterizing uncertainty, measurement of adaptation, cross-study comparison, and use of empirical estimates to project the impact of future climate change. I conclude by discussing remaining methodological challenges.

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The Economic Impacts of Climate Change

Abstract: have been a great help in the construction of Table 1. Three anonymous referees and Suzanne Leonard provided excellent comments. All errors and opinions are mine.

Cited by 569 publications

References 132 publications

Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Fair weather or foul? The macroeconomic effects of El Niño

Climate Econometrics

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