The economic impact of climate change in the 20th and 21st centuries
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The national version of FUND3.6 is used to backcast the impacts of climate change to the 20th century and extrapolate to the 21st century. Carbon dioxide fertilization of crops and reduced energy demand for heating are the main positive impacts. Climate change had a negative effect on water resources and, in most years, human health. Most countries benefitted from climate change until 1980, but after that the trend is negative for poor countries and positive for rich countries. The global average impact was positive in the 20th century. In the 21st century, impacts turn negative in most countries, rich and poor. Energy demand, water resources, biodiversity and sea level rise are the main negative impacts; the impacts of climate change on human health and agriculture remain positive until 2100.
There is a substantial literature about the future impacts of climate change (Cline 1992b; Fankhauser 1995; Mendelsohn et al. 2000; Nordhaus 1991; Tol 2002a); see (Tol 2009b) for an overview. Less is known, however, about the impacts of climate change in the past. While there is no immediate policy relevance of estimates of past effects—as liability is yet to be established (Tol and Verheyen 2004)—such estimates would serve to validate models of future impacts—and thus help to improve these models and build confidence. In this paper, I turn this question on its head. I use a model to backcast past impacts, thus generating hypotheses to be tested against observations.
Unfortunately, there are no direct observations of the economic impact of past climate change. Note that the cause of climate change, past or future, is irrelevant for its impacts. There are, however, some studies that estimate particular aspects of the impact of past climate change, typically focussing on biophysical impacts.
The literature on natural disasters is perhaps most advanced (Barredo 2009; Changnon and Changnon 1998; Changnon 2003; Changnon and Changnon 1992; Neumayer and Barthel 2011; Pielke 2005; Pielke et al. 2008; Ryan et al. 2011). These studies typically conclude that trends in the damage done by natural disasters are largely, if not entirely, the result of increases in the number of people and their wealth. It should be noted, though, that these studies rely on ad hoc normalisation rather than multiple regression (Toya and Skidmore 2007).
Estimates of the impact of past climate change on crop yields generally find a significant effect, but one that is small relative to other trends in agriculture; impacts are positive or negative depending on crop and location (Holmer 2008; Lobell et al. 2005; Lobell et al. 2011; Lobell and Asner 2003; Lobell and Field 2007; Myneni et al. 1997; Nicholls 1997; Tao et al. 2006; Tao et al. 2008; Twine and Kucharik 2009; Zhang et al. 2010). Carbon dioxide concentrations vary little over space and only slowly over time, so there is little statistical evidence of its impact on crop yields. Experimental evidence, however, points to a positive impact (Long et al. 2006).
The impact of past climate change on malaria has also been the subject of intense debate (Byass 2008; Chaves and Koenraadt 2010; Craig et al. 2004; Gething et al. 2010; Hay et al. 2002a; Hay et al. 2002b; Loevinsohn 1994; Small et al. 2003; Thomas 2004). Overall, there is agreement that climate change is not the main driver of the spread of malaria; some people argue it has a small effect while others argue the effect is negligible. The story is the same for diarrhoea—another big killer that is sensitive to weather and climate—but there is less evidence (Lloyd et al. 2007). There is empirical evidence for negative health impacts of both heat and cold stress (D’Ippoliti et al. 2010; Martens 1998; McMichael and Dear 2010). The net impact is different across space (EUROWINTER Group 1997) and over time (Carson et al. 2006; Davis et al. 2002; Davis et al. 2003).
Empirical research into the effect of climate change on energy demand resembles that of heat and cold stress: There are many case studies (Giannakopoulos and Psiloglou 2006; Henley and Peirson 1998; Moral-Carcedo and Vicens-Otero 2005; Pardo et al. 2002; Sailor and Munoz 1997), but few multi-country studies (Bessec and Fouquau 2008) and few studies that cover a longer time-period (Considine 2000; Hekkenberg et al. 2009). The latter studies are, of course, best suited for the detection of structural patterns that would allow extrapolation into the future. These studies find that warming would lead to a decrease of energy demand in winter and an increase of energy demand in summer. The relative magnitude of these two opposite effects depends on socio-economic circumstances and the climatic starting point.
Statistical analyses of climate and water resources are typically done for single river basins. There are a few studies that cover a wider area (Kundzewicz et al. 2005; Lindstroem and Bergstroem 2004; Lins and Slack 1999; Svensson et al. 2005). These studies typically conclude that every river responds differently to changes in precipitation and temperature.
In sum, the empirical literature on the impacts of climate change finds mixed effects. Unfortunately, none of these studies aggregates the impacts, so that it is difficult to say whether past climate change was positive or negative. Below, I will use a model, FUND, to answer that question.
The paper proceeds as follows. Section 2 presents the data and the model. Section 3 discusses the results. Section 4 concludes, paying particular attention to the testable hypotheses that emerge from this paper.
2 Data and model
National data on population and income for 1960–2000 are taken from EarthTrends by the World Resources Institute.1 For 1900–1960, regional data are from (Maddison 1995). I assumed equal national growth rates within regions. I used HadCRUT3 for the global mean temperature2 and carbon dioxide concentration from CDIAC.3
I use the Climate Framework for Uncertainty, Negotiation, and Distribution (FUND), version 3.6 in its national resolution. The continental version of FUND is a fully integrated model, including scenarios of population, economy, energy use, and emissions; a carbon cycle and simple climate model; and a range of impact models. The national version of FUND (Link and Tol 2011) only covers the impacts of climate change—while population etc. are as observed for the 20th century and exogenous for the 21st century.
Version 3.6n of FUND corresponds to version 1.6 (Tol et al. 1999; Tol 2001; Tol 2002c) except for the impact module described in (Link and Tol 2004; Narita et al. 2009; Narita et al. 2010; Tol 2002a; Tol 2002b) and carbon cycle feedbacks taken from (Tol 2009a). A full list of papers and the technical documentation for the model can be found online at http://www.fund-model.org/. The model code for this paper is at: http://dvn.iq.harvard.edu/dvn/dv/rtol. The complete set of results is there as well as, including monetised impact estimates for all sectors and physical impact estimates for mortality, morbidity and sea level rise.
The model runs from 1900 to 2100 in time steps of 5 years. The model is initialised for 1895.
The climate impact module (Tol 2002a; Tol 2002b) includes the following categories: agriculture, forestry, sea level rise, cardiovascular and respiratory disorders related to cold and heat stress, malaria, dengue fever, schistosomiasis, diarrhoea, energy consumption, water resources, unmanaged ecosystems, and tropical and extra tropical storms. This list of impacts is not exhaustive, but other impacts have yet to be consistently quantified and monetized at the global scale—see (Tol 2009b; Tol 2009c) for a discussion of missing impacts and speculation on their sign and size. Climate change related damages can be attributed to either the rate of change (i.e., the annual change in temperature, benchmarked at 0.04 °C/year) or the level of change (i.e., the temperature change since pre-industrial times, benchmarked at 1.0 °C). Sectors impacted by climate change also change for other reasons, but the impacts shown below are the impacts of climate change only. Impacts are impacts on welfare, expressed in dollars at market-exchange rates. Damages from the rate of temperature change slowly fade, reflecting adaptation (Tol 2002b).
People can die prematurely due to climate change, or they can migrate because of sea level rise. Like all impacts of climate change, these effects are monetized. The value of a statistical life is set to be 200 times the annual per capita income. The resulting value of a statistical life lies in the middle of the observed range of values in the literature (Cline 1992a). The value of emigration is set to be 3 times the per capita income (Tol 1995). Losses of dryland and wetlands due to sea level rise are modeled explicitly. The monetary value of a loss of 1 km2 of dryland was on average $4 million in OECD countries in 1990 (Fankhauser 1994). Dryland value is assumed to be proportional to GDP per square kilometer. Wetland losses are valued at $2 million per square kilometer on average in the OECD in 1990 (Fankhauser 1994). The wetland value is assumed to have logistic relation to per capita income. Coastal protection is based on cost-benefit analysis, including the value of additional wetland lost due to the construction of dikes and subsequent coastal squeeze.
Other impact categories, such as agriculture, forestry, energy, water, storm damage, and ecosystems, are directly expressed in monetary values without an intermediate layer of impacts measured in their ‘natural’ units (Tol 2002a). Impacts of climate change on energy consumption, agriculture, and cardiovascular and respiratory diseases explicitly recognize that there is a climatic optimum, in which welfare is maximum. The location of the optimum is determined by a variety of factors, including physiology, infrastructure and behaviour. Impacts are positive or negative depending on whether the actual climate conditions are moving closer to or away from the optimum climate. Impacts are larger if the initial climate conditions are further away from the optimum climate. The optimum climate is of importance with regard to the potential impacts. The actual impacts lag behind the potential impacts, depending on the speed of adaptation. The impacts of not being fully adapted to new climate conditions are always negative (Tol 2002b).
The impacts of climate change on coastal zones, forestry, tropical and extratropical storm damage, unmanaged ecosystems, water resources, diarrhoea malaria, dengue fever, and schistosomiasis are modelled as simple power functions. Impacts are either negative or positive, and they do not change sign (Tol 2002b). All impact functions are smooth. There are no threshold effects.
Vulnerability to climate change changes with population growth, economic growth, and technological progress (Yohe and Tol 2002). Some systems are expected to become more vulnerable, such as water resources (with population growth), heat-related disorders (with urbanization), and ecosystems and health (with higher per capita incomes). Other systems such as energy consumption (with technological progress), agriculture (with economic growth) and vector- and water-borne diseases (with improved health care) are projected to become less vulnerable at least over the long term (Tol 2002b). The income elasticities (Tol 2002b) are estimated from cross-sectional data or taken from the literature.
Heat and cold stress are assumed to have an effect only on the elderly, non-reproductive population. In contrast, the other sources of mortality also affect the number of births. Heat stress only affects the urban population (because of the urban heat island effect and the combination of poverty, isolation and crime). The share of the urban population among the total population is based on the World Resources Databases (http://earthtrends.wri.org). It is extrapolated based on the statistical relationship between urbanization and per capita income, which are estimated from a cross-section of countries in 1995.
Some impact estimates are based on relatively mature research—health, agriculture, forestry, sea level rise—while other impacts draw on less advanced studies—storms, ecosystems, energy, water. Some impacts are missing altogether—air quality, violent conflict, labour productivity, tourism and recreation. Weather variability is poorly accounted for, and potential changes in weather variability ignored. The model assumes that there are few barriers to adaptation. There are no interactions between the impact sectors, and there are no higher order effects on markets or development. Such is, unfortunately, the state of the art.
The aggregate impact of global warming on water resources is negative. The relationship to accumulative warming is roughly linear in temperature in the first half of the 20th century, but the negative effect is alleviated by improved water use efficiency and economic growth4 in the second half of the 20th century and the 21st century. Impacts are about −0.1 % of GDP in 2000 and edge up to −0.2 % of GDP. Although the overall impact is negative, national impacts are mixed with some countries benefitting in some periods. Qualitatively, therefore, the model is not inconsistent with the empirical literature reviewed in the introduction.
The health impact of climate change is mixed. Generally speaking, cooling brings net benefits, moderate warming brings both benefits and damages, while larger warming brings net damages. This is because FUND has a number of health impacts, some related to cold and some to heat—see below—and some related to the level of climate change and others to its rate. Furthermore, health impacts depend on the age structure of the population and income (a proxy for health care), while the value also depends on income. The result is the complex pattern shown in Fig. 3. By the end of the 20th century, the impact is clearly positive, equivalent to an income gain of 0.4 %. Impacts continue to increase slightly till 2055 and then begin to fall to reach 0.3 % in 2100.
The impact of global warming on energy consumption is positive during the 20th century. While the demand for cooling in summer increased, this is more than offset by the reduction in the demand for heating in winter. Towards the end of the 20th century, the annual savings on energy amount to almost 0.4 % of GDP. The national impacts are mixed, with losses and gains in different places and times, qualitatively corresponding to the literature surveyed above. After 2010, however, the demand for cooling starts to rise rapidly while the reduction in demand for heating levels off. The result is a large negative impact, reaching −1.9 % of GDP by 2100.
Agriculture is the biggest positive impact in the 20th century, approaching 0.8 % of GDP by 2000. This is entirely due to carbon dioxide fertilization, which makes crops grow faster and more water efficient. The impact of climate change (temperature, precipitation, cloud cover, wind, etc.) is actually negative, reaching −0.3 % in 2000. Because carbon dioxide fertilization is the dominant effect, impacts are positive for all countries and periods. This is in contrast to the empirical literature reviewed in the introduction—although it should be noted that that literature is limited to crop yields while the results here are for agricultural production. Impacts continue to increase to 1.3 % in 2055. After that, impacts begin to fall as carbon dioxide fertilization begins to saturate and the negative impacts of warming grow larger. By 2100, however, impacts are still positive (0.8 % of GDP).
Figure 2 shows world aggregate economic impact, which ranges between −0.2 and 1.4 % of GDP in the 20th century and between −1.2 % and 1.4 % of GDP in the 21st century. Figure 2 also shows the maximum and minimum impact across countries.5 The range is large with high positive impacts (~100 %) for the rapid cooling around 1910 and high negative impacts (~ −120 %) for the rapid warming around 1940—health is the main driver of these large impacts (see below). Overall, the maximum and minimum are two orders of magnitude larger (in absolute terms) than the average. In the 21st century, the range of impacts is much narrower. Nonetheless, a lot of variation is hidden by the mean. In 2100, for instance, impacts range from −18 % to +6 % of GDP—roughly an order of magnitude larger than the mean. This suggests that the variability of the climate in the 20th century drives the distribution of impacts across countries.
Figure 2 further shows the equity-weighted impacts.6 These are more positive in the 20th century than the non-weighted average, indicating that poorer countries see greater benefits than richer countries—primarily due to carbon dioxide fertilization of agriculture. However, the gap begins to close in 1980—the maximum impact of climate change occurs 45 years earlier when equity weighted. The equity-weighted impacts fall rapidly to reach −2.8 % in 2100.
A number of countries stand out. China benefits most from climate change on average across the 20th century. Agriculture is a big positive, followed by energy use and water resources. Haiti is the second biggest beneficiary. This is entirely due to the positive effects of carbon dioxide fertilization on agriculture. Averaged over the 20th century, each African nation loses out. n the first half of the century, however, there are both winners and losers. The main positive factor is the large impact on agriculture. Negative impacts on health outweigh agriculture in the second half of the 20th century.
Timor Leste is hurt most by climate change on average across the 20th century. This is largely due to the impacts of climate change on poverty-related health problems, particularly diarrhoea and malaria. Bangladesh is the most vulnerable country outside of Africa. It sees relatively large negative impacts on its health, coastal zone, and water resources—which are larger than the positive impacts on agriculture and energy use. Russia also stands out. Although there are large damages due to a climate-change-induced increase in water scarcity, this is more than offset by benefits for energy use, agriculture and human health.
4 Discussion and conclusion
Previous studies have found that moderate, future climate change would bring net benefits (Tol 2009b). This study finds that past, moderate climate change brought net benefits. This is no surprise as the current study infrapolates from previous work.7 Carbon dioxide fertilization of crops and reduced energy demand for heating are the main positive impacts. Climate change had a negative effect on water resources and (by and large) human health. Most rich and most poor countries benefitted from climate change until 1980, but after that the trend is negative for poor countries and positive for rich countries. In the 21st century, impacts turn negative in most countries, rich and poor. Future climate change is a reason for concern.
A number of testable hypotheses arise from these results. Energy demand and agricultural production are relatively well-understood. While statistical analyses have focussed on recent decades for which data are excellent, it should be feasible to use older data to test the impact of climate change. The same should be possible for water resources in at least some parts of the world. For health, data availability would allow for selected case studies only. Care should be taken that like is compared with like. For example, FUND considers agricultural production while empirical studies tend to focus on crop yields.
I briefly reviewed the empirical literature in Section 1. Not surprisingly as FUND is calibrated to that literature, the model backcasts are roughly in line with the data. The impact of climate change on tropical cyclone damages is small in both model and observations. The impact on human health varies but is more often negative than positive in both model and observations. The impact on water resources is predominantly negative in the model, while the observations suggest a mixed impact. The impact on agriculture is predominantly positive in the model, while the observations suggest a mixed impact. The impact on energy use is positive is both model and observations. Note, however, that a direct comparison between the model results and the empirical results is not possible, because coverage and scope are different, and the empirical studies focus on different indicators.
The results for the 20th and 21st century were derived with the same model, and impact patterns are therefore similar. However, the spread of impacts is larger in the 20th century than in the 21st century. This is because climate variability is considered in the 20th century but not in the 21st. This would suggest that the spread of the impacts is underestimated for the 21st century. This result affects not only FUND, but all models of the economic impact of climate change.
The exercise presented here should be repeated with other models of the impact of climate change. Surveys and meta-analyses of the empirical literature on the impact of climate change should be conducted to created indicators that can be directly compared to the model results. This is needed to build confidence in the models that are used to assess the magnitude of the problem of climate change.
Water is a necessary good. The assumed income elasticity is 0.85, that is, a 10 % increase in per capita income leads to an 8.5 % increase in the per capita value of water.
Note that Figure 2 shows the worst off and best off countries at each point in time, rather than the worst off and best off countries averaged over the centuries.
National impacts are weighted with the ratio of world average per capita income and national per capita income. This corresponds to a logarithmic function for individual utility and utilitarian social welfare (Fankhauser et al. 1997).
Infrapolation is extrapolation to the past.
I am grateful to the Copenhagen Consensus Center and the EU ClimateCost project for financially supporting this research. Two anonymous referees had excellent comments.
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