A new paper ‘Economic Impact of Energy Consumption Change Caused by Global Warming’ [1] by Peter Lang and Ken Gregory, published in the peer-reviewed journal Energies, tests the validity of the FUND model’s energy impact functions. Empirical data of energy expenditure and average temperatures of the US states and census divisions are compared with projections using the energy impact functions with non-temperature drivers held constant at their 2010 values. The empirical data indicates that energy expenditure decreases as temperatures increase, suggesting that global warming may reduce US energy expenditure and thereby have a positive impact on US economic growth. FUND projects the economic impact of 3 °C of global warming from 2000 to be −0.80% of GDP, whereas our analysis of the EIA data indicates the impact would be +0.07% of GDP. The US states span the latitudes where 82% world’s GDP was produced in 2010. We infer that the impact of global warming on energy consumption may be positive for the regions that produced 82% of the world’s GDP and, by inference, may be positive for the global economy. If FUND projections for the non-energy impact sectors are valid, 3 °C of global warming from 2000 would increase global economic growth.

This post consists largely of excerpts from Lang and Gregory (2019). The paper does not address the causes of global warming; only the impacts of warming should future warming occur.

FUND is an Integrated Assessment Model (IAM) that is used to estimate the economic impact of greenhouse gas emissions and global warming. IAMs approximately reproduce the temperature projections from the global climate models and apply impact functions to estimate the economic impacts of global warming. Various studies conclude that the impact functions used in the IAMs are derived from inadequate empirical data. FUND is one of the three most cited IAMs. 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). The negative overall impact projected by FUND is mostly due to one impact sector – energy consumption. However, the projected negative impact is at odds with empirical data.

There are many drivers of change in energy consumption other than temperature change. We investigate the relationship between temperature change and energy expenditure change by removing the impacts of the non-temperature drivers. We do this by using the US Energy Information Administration (EIA) empirical data from single-year surveys for one country, the US. This method has a number of advantages. Drivers of energy expenditures over a short period are relatively constant with time. These drivers include the number, age and floor area of buildings, climate, gross domestic product (GDP), population, technology and energy prices. The effects of adaptation to local climate are included because buildings and behaviours are adapted for their climate. The USA has a relatively uniform standard of living, so per capita energy expenditures can be related to state average temperatures without need to adjust of differences in GDP/capita.

The analysis is done for space heating (SH) and space cooling (SC), and for residential (Res) and commercial (Com) buildings. Energy expenditures include energy types: electricity, natural gas, heating oil, propane/LPG and district heating. We calculate the $/capita expenditures of residential buildings for 27 states and state groups, and of residential and commercial buildings for nine census divisions. Figure 5 of the paper shows the US$ per capita of residential SH and SC expenditures versus temperature for the 27 states and state groups.

Figure 1 below reproduces figure 5 of the paper.

Figure 1: Residential per capita space heating and space cooling expenditure versus temperature at the population centroid, for 27 states and state groups.

Table 1 presents the total US economic impact, at 3 °C GMST increase, estimated from the residential and commercial buildings data.

Table 1 from the paper is reproduced below.

We compared the economic impact of a 3 °C global mean surface temperature (GMST) increase from 2000 on US space heating and cooling energy expenditures determined from empirical data to the projections from FUND with non-temperature drivers constant at 2010 values, expressed as percent of year 2010 GDP. Table 2 below is a portion of table 2 of the paper.

Table 2 shows that the FUND energy impact functions, with non-temperature drivers at 2010 values, project that the impact on the US economy would be –0.80% of GDP, whereas the analysis of the EIA data finds +0.07%. These results are opposite in sign and the difference is 0.87% of GDP; that is, the FUND impact functions project that the impacts would be about thirteen times worse than the EIA data indicates. The cooling component contributes most of the difference.

Figure 2 below reproduces the abstract’s graphic which shows the two projections. It is similar to figure 9 of the paper.

Figure 2: Economic impact of energy use change due to warming as projected by FUND compared to empirical data. Non-temperature drivers are held constant at 2010 values.

We also investigated the projected space heating and cooling expenditure impacts of warming of all the 16 FUND World-regions with non-temperature drivers held constant at 2010 values. The projected economic impacts are negative for 13 of the 16 FUND regions. The projected negative impacts increase as temperature change increases. These are contrary to findings from the EIA data for the USA. This suggests the FUND energy impact functions are misspecified.

We compare the proportions of the FUND projected energy impacts that are due to temperature change of 3 °C GMST from 2000 to non-temperature drives over the same period for the USA, China Plus , and the World as shown in table 4 of the paper. [The China Plus region includes China, Hong Kong, North Korea, Macau and Mongolia.] We find that 67% of the world’s total energy impacts are due to non-temperature drivers. An analysis of US and China Plus regions leads us to suspect that the calibration of energy impact functions in FUND with respect to temperature is contaminated by impacts due to non-temperature drivers.

The savings on heating expenditures are assumed in FUND to saturate whereas cooling expenditures are assumed to accelerate as it gets warmer. However, the EIA data show that both heating and cooling expenditures are near linear against temperature from 6 °C to 22 °C for the USA. The SH and SC functions in FUND are calculated with respect to the GMST rather that the temperatures at each region. This causes the heating savings from warming to saturate at low temperatures in cold regions such as Canada, but saturate only at high temperature in tropical regions, which does not make sense. This issue will be discussed in a following post.

Figure 15 in the paper, reproduced below as figure 3, plots the global economic impacts by impact sector as a function of GMST change from 2000 to a 3 °C temperature increase as projected by FUND with non-temperature drivers included. The total of all impact sectors, and the total excluding energy, are also shown. Excluding energy is equivalent to assuming that the SH expenditure savings are equal to the SC expenditure increases.

Figure 3: FUND projected global sectoral economic impact of climate change as a function of GMST change from 2000. Total* is of all impact sectors except energy.

With energy impacts excluded, FUND projects the global impacts to be +0.2% of GDP at 3 °C GMST increase from year 2000. With the energy impact functions misspecifications corrected and all other impacts as projected, the projected total economic impact may be more positive.

The economic impact of climate policies is likely to be substantial. It is the sum of the economic impact of the policies and the cost of implementing and maintaining the policies. If global warming is beneficial, as this study indicates may be the case, then the total economic impact is the sum of the forgone benefits of the avoided global warming plus the cost of policies to mitigate warming.


Our analysis suggests that the overall impact of global warming may be positive—that is, it would increase global economic growth. If this is correct, then the positive impacts can be maximised and the negative impacts minimised by increasing wealth, but not by reducing global warming. Bjørn Lomborg estimates the mitigation cost at about US$1.8 trillion per year in 2030.


Our analysis of empirical data finds that the global warming of 3 °C relative to 2000 would decrease US energy expenditure and would have a positive impact on US economic growth. FUND projects the economic impact to be −0.80% of GDP, whereas our analysis of the EIA data indicates the impact would be +0.07% of GDP. We infer that the impact of global warming on energy consumption may be positive for the regions that produced 82% of the world’s GDP in 2010 and, by inference, may be positive for the global economy.


The significance of these findings for climate policy is substantial. If the FUND sectoral economic impact projections, other than energy, are correct, and the projected economic impact of energy should actually be near zero or positive rather than negative, then global warming of up to around 3 °C relative to 2000, and 4 °C relative to pre-industrial times, would be economically beneficial, not detrimental.

In this case, the hypothesis that global warming would be harmful to the global economy this century may be false, and policies to reduce global warming may not be justified. Not adopting policies to reduce global warming would yield the economic benefits of warming, CO2 fertilization of crops and forests, and avoid the economic costs of those policies.

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The paper was published 19 September 2019. The editors made substantial changes to the paper after it was accepted for publication. This included putting figures 3 – 8 together rather than separated by the explanatory text, moving Appendix C into the body of the paper which degraded the flow of the paper, and several formatting issues. At our request, Appendix C was put back, but the process caused errors in the References. We listed further corrections that were needed. The paper was published without the authors’ approval of the final proofread version despite us saying “do not publish the paper until we have approved the final version”. Energies made most of our requested changes to the PDF version. We have sent several emails to the Managing Editor and the Editor in Chief requesting that the required corrections be made to the HTML version, but Energies refused to make the correction.


The HTLM version has 5 references that link to the wrong URL (specifically references 2, 3, 13, 23, 24). They are correct in the PDF version. Persons wanting to read the references should open them from the PDF version. The HTML version still has six figures together rather than separated by the explanatory text, and several formatting issues. Readers may find Section 3.1 easier to follow in the PDF version than in the HTML version.

The PDF version of the paper is here.

Reference

  1. Lang, Peter A.; Gregory, Kenneth B., Economic Impact of Energy Consumption Change Caused by Global Warming, Energies 2019, 12, 18, 3575. https://doi.org/10.3390/en12183575 , Licensee MDPI, Basel, Switzerland.