1. Introduction

There is a scientific hypothesis and political acceptance [ 1 ] that global warming of 2 °C or more above pre-industrial times would have a negative impact on global economic growth. This hypothesis is supported by economic models that rely on impact functions and many assumptions. However, the data needed to calibrate the impact functions is sparse, and the uncertainties in the modelling results are large [ 2 3 ]. The negative overall impact projected by at least one of the main models, Climate Framework for Uncertainty, Negotiation and Distribution (FUND) [ 4 ], is mostly due to one impact sector – energy consumption. However, the projected negative impact seems to be at odds with empirical data. If this paper’s findings from the empirical energy consumption data are correct, and if the impact functions for the non-energy sectors are correct, then the overall economic impact of global warming would be beneficial. If true, the implications for climate policy are substantial.

Integrated Assessment Models (IAM) approximately reproduce the projections from the Global Climate Models (GCM) and apply impact functions to estimate the economic impacts of global warming. The impact functions are derived from and calibrated to what the developers assess are the most suitable studies of the impacts. The impact functions require many assumptions, including projections of population, gross domestic product (GDP), per capita income, elasticities and technology progress in energy provision.

Various studies conclude that the impact functions (also called damage functions) used in the IAMs are derived from inadequate empirical data. For instance, Pindyck [ 5 ] (p. 11) says “when it comes to the damage function, however, we know almost nothing, so developers of IAMs can do little more than make up functional forms and corresponding parameter values. And that is pretty much what they have done.” According to Kolstad et al. [ 2 ] (p. 258), the IAM damage functions “are generated from a remarkable paucity of data and are thus of low reliability”. The National Academies of Sciences, Engineering and Medicine (NAS) [ 3 ] (p. 261) says FUND [ 4 ] needs further justification for the damage functions, the adaptation assumptions for the different sectors, the regional distribution of damages, and the parametric uncertainties overall. Tol [ 6 ] says the impact of climate change has not received sufficient attention; he says “there is either very little solid evidence, no conclusive evidence, or no quantification of welfare impacts”.

NAS [ 3 ] reviews the damage functions of the three main IAMs, discusses alternative approaches, reviews recent literature on damage estimation, and offers recommendations for a new damage module. It says that much of the literature on which the damage functions are based is dated and, in many cases, does not reflect recent advances in the scientific literature. For example, the FUND energy impact parameters are calibrated to reproduce the results of the 1996 papers by Downing et al. [ 7 8 ] and the income elasticity results of the 1995 paper by Hodgson and Miller [ 9 ].

FUND [ 4 ] is one of the three most cited IAMs; Bonen et al. [ 10 ], National Research Council [ 11 ] and NAS [ 3 ] compare them. FUND is the most complex. FUND disaggregates by sixteen world regions and eight main impact sectors (agriculture, forestry, water resources, sea level rise, ecosystems, health, extreme weather, and energy consumption). This enables analysts to conduct sensitivity analyses and to separately test individual impact functions.

Tol [ 12 ] used the national version of FUND3.6 to backcast the economic impact of global warming for these sectors for the 20th century and projected the impacts for the 21st century. Tol fitted the backcast results to observations of 20th century sectoral impacts (Tol [ 13 ]). Tol [ 12 ] is an important study because it estimates the impacts for the most significant impact sectors, globally and by region. It also estimates the total impact on all sectors. Figure 1 (adapted from Figure 3 in Tol [ 12 ]) shows the backcast (observed) and projected economic impact of global warming by sector. (We have added the ‘Total’ and ‘Total*’ lines. ‘Total’ is digitized from Tol [ 12 ] Figure 2. ‘Total*’ is the sum of all the backcast (observed) historical impacts from 1900 to 2000, and all projected sectoral impacts except energy from 2000 to 2100).

The bottom panel of Figure 1 suggests that an increase of up to around 4 °C Global Mean Surface Temperature (GMST) above pre-industrial times would be beneficial for the total of all sectors if the projected energy impacts for 2000–2100 are excluded. Energy consumption is projected to have a substantial negative impact during the 21st century; in fact, its negative impact exceeds the total impact of all other sectors, which is positive, from about 2080.

The striking change in trend of the energy impact at the turn of the century inspired this study. The trend was positive as GMST increased by 0.61 °C during the 20th century [ 13 ] but FUND projects it will be substantially negative for the 21st century as GMST is projected to increase further. That is, whereas the observations for 1900–2000 show the impacts were positive, FUND projects continued global warming would have negative impacts for the global economy.

Contrary to the FUND energy projection for the period 2000–2100, the US Energy Information Administration (EIA) [ 14 15 ] empirical data appear to indicate that global warming would reduce US energy expenditure and, therefore, contribute positive economic impacts for the USA. The paper infers that the impacts of global warming on the US economy may be indicative of the impacts on the global economy.

If the economic impact of energy is near zero or positive, and if the total of the sectoral projections in Figure 1 , other than for energy consumption, is approximately correct, global warming would be beneficial up to around 3 °C relative to 2000, and 4 °C relative to pre-industrial times. The significance of these findings for climate policy is substantial. For instance, policies that aim to reduce global warming would not be economically justifiable. Therefore, the economic impact of energy consumption projected in Tol [ 12 ], and by FUND3.9, warrants investigation if FUND is to be used for policy.

This paper tests the validity of the FUND energy impact functions against US empirical data. It examines EIA data for the USA to investigate whether the impact of global warming on US energy consumption would reduce or increase US economic growth and compares the results with the energy projections. Next it investigates the projections for FUND’s 16 world regions. Lastly, it discusses some policy implications.