Economic carbon cycle feedbacks may offset additional warming from natural feedbacks

Our results indicate that the economic feedback has the potential to reverse the sign of the overall carbon–climate feedback, but the significance of the effect is highly sensitive to the relationship between climate and GDP. If the effect of climate on GDP is large and dominates the feedback, the economic carbon feedback counteracts the response of the natural carbon cycle. However, if this temperature–GDP effect is more in line with estimates like those in the 2016 version of DICE model (11), we can expect that the economic contribution to the carbon–climate feedback will instead add slightly to the natural positive gain (Fig. 4), somewhat increasing future temperatures and atmospheric carbon dioxide (Fig. 3A).

Our estimate of climate effects on fossil emissions is substantially higher than a previous analysis from the Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) model, which found a reduction in CO₂ emissions of 4.7% from their economic feedbacks by 2100 (9). This is likely driven by the choice of economic damage function. The damages found by Burke et al. (19) are larger than those used in ENVISAGE as well as those used in many other models because Burke’s analysis broadly includes any climate-driven effects that would be reflected in GDP over the past half century. The sum of the effects considered in ENVISAGE we expect to be lower because only certain economic sectors were included. For example, both extreme weather and catastrophic events are not included in the ENVISAGE simulations (29), whereas the GDP damages from Burke et al. (19) are general enough to include such effects.

By propagating upper and lower uncertainty bounds for each term in the Kaya identity through our model, we have attempted to illustrate the spread of potential outcomes. Additionally, although we have made every effort to use reasonable values, it was necessary to make several major assumptions to maintain the simplicity of our model and not attempt to replicate a full integrated assessment model, because that is beyond the scope of this work. A key future challenge is to quantify economic carbon–climate feedbacks within and across integrated assessment models that account for more complex interactions among different sectors and processes.

Improving estimates of the economic carbon–climate feedback is particularly relevant because important tradeoffs exist with respect to the societal effects of strong versus weak economic damage functions. Although a stronger damage function in response to rising temperature appears to imply that it may be easier to match emissions reductions targets, this comes at an economic cost that would likely make it more difficult for vulnerable regions to respond to climate change effects (30). Moreover, such economic and social costs entailed by stronger damage functions are likely to be large and inequitably distributed because climate change is expected to worsen already existing economic vulnerabilities (31). Natural disasters, for example, have higher death tolls in lower-income areas and in countries without democratic institutions (32). In our globally averaged model, the Burke et al. relationship led to GDP losses of 22% by 2100 (SI Appendix, Table S2). In just the United States by the end of the century, the poorest third of counties are predicted to experience losses of 2–20% of income, whereas the richest third may experience losses of only 7% up through potential benefits of 1.2% of income (33). Any potential benefit in terms of lower emissions from a negative economic feedback only exists because nations necessarily lose so much productivity, in the form of human lives, agriculture, infrastructure, and labor, that this reduction in economic activity lowers their fossil fuel emissions.

Strong versus weak economic damage functions also may have implications for the distribution of climate effects across natural and human systems. A weaker economic damage function, for example, would allow more CO₂ to accumulate in the atmosphere, causing higher surface air temperatures. Accelerated warming, in turn, would cause greater damages in terrestrial and marine ecosystems, including losses of net primary production and biodiversity on land (34) and the disruption of critical nutrient supply pathways in the ocean (35). Thus, although natural and economic feedbacks are likely opposite in sign, carbon–climate feedbacks driven by higher temperatures have net damaging effects on both natural and human systems.

The strength of both economic and natural feedbacks varies significantly over the globe, so regional carbon cycle effects may be considerably stronger or weaker than the global mean (19, 36). Economic activities driving the carbon–climate feedback at the local level will include changes in tourism revenue, damages from sea level rise and wildfires, and locally varying patterns of energy use. For example, Isaac and Van Vuuren (37) found that India showed a very strong effect of temperature on energy demand, in contrast to their finding of a much less significant effect globally. The economic climate feedback from energy use would overall be expected to be higher in areas with quickly increasing GDP and population as well as larger predicted climate effects.

In the model used here, we have considered a limited number of both natural and economic processes. We tuned our simple natural carbon cycle model to match the mean behavior of the CMIP5 models, but these models are missing key natural processes such as the permafrost carbon reservoir and its sensitivity to thaw (28) and are weak in their representation of other drivers of the carbon–climate feedback including the representation of ecological tipping points within the Amazon (38). On the economic-driven side, we do not include any feedbacks associated with climate effects on land use. Recent work indicates these would be expected to contribute to a positive economic carbon–climate feedback (39), mitigating slightly the negative effects of the GDP feedback described here. It is also worth acknowledging that there are other human-driven feedbacks that fall outside of the carbon–climate feedback. One example is an economic carbon–concentration feedback associated with the benefits of increasing atmospheric CO₂ on crops. There are also potential economic effects associated with climate-driven human migration, which could have varied effects on climate through both carbon and noncarbon pathways. Beyond carbon feedbacks entirely, there may be policy-driven feedbacks that influence aerosols and albedo.

Our results provide a baseline effort to assess the economic carbon–climate feedback and compare it to the natural feedback by unifying the different contributing mechanisms and processes within a single framework. More broadly, we show how methodology for carbon cycle feedback analysis can be extended to the economic sector, for future assessment of integrated assessment models. Our model results have demonstrated that an economic carbon–climate feedback has the potential to significantly counteract the warming contribution of land and ocean feedbacks; however, the benefits of this negative economic feedback in terms of the carbon cycle will likely be offset by substantial economic and societal costs. Earth system models that neglect these economic feedback processes may significantly overestimate the carbon–climate feedback. Future research to better characterize the nature and scale of economic disruptions from climate change will reduce uncertainty and allow this feedback to be better incorporated into integrated assessment and Earth system models.

Details of our analytic method are available in SI Appendix, SI Materials and Methods. All code used to generate the results is available on GitHub (40). Briefly, we represent the natural carbon cycle—including key carbon–climate and carbon–concentration feedbacks—using a global box model of the atmosphere, land, and ocean carbon system (SI Appendix, SI Materials and Methods). We tuned the model to within 1 SD of the mean behavior of Earth system models from the CMIP5 (SI Appendix, Tables S4 and S5), and it reasonably reproduces observations of the carbon cycle and temperature over the past 2 centuries (SI Appendix, Fig. S1). Economic feedback effects are explicitly incorporated in the model as effects on different factors of the Kaya identity:

where F represents global fossil fuel CO₂ emissions; P is population; G is world GDP or gross world product; E is global energy consumption; and $E/G$ and $F/E$ are the energy intensity of GDP and the carbon intensity of energy, respectively. As a baseline, we use historical socioeconomic data (SI Appendix, Fig. S2 and Table S6) and assume future fossil fuel CO₂ emissions and energy and population projections from the GCAM simulation for RCP8.5 (25, 26). Relationships with temperature for each economic component are derived from previous studies (SI Appendix, SI Materials and Methods and Fig. S3).

We isolate and estimate the magnitude of carbon cycle feedbacks by restricting in turn the various components of the coupled model following methodology established for natural carbon cycle analysis (5, 8). All scenarios include natural carbon–concentration feedback processes, but carbon–climate feedbacks are isolated in different scenarios as summarized in SI Appendix, Table S1. The no feedbacks scenario is our baseline for comparison and includes only natural carbon cycle responses to rising atmospheric CO₂, neglecting both human responses and land and ocean responses to warming. The net natural scenario corresponds to the fully coupled scenario in previous analyses of the natural carbon cycle (4, 5) in which all natural feedbacks are allowed to operate, but all economic responses to warming are excluded. The population scenario adds estimates of climate-related deaths (but no other human responses) (41) onto the baseline scenario. The energy intensity scenario includes only modeled changes in energy demand for heating and cooling of residential and commercial buildings (following ref. 37) on top of the baseline. The carbon intensity scenario includes only temperature-related changes in the efficiency of electricity production, electricity distribution, and transportation (16, 17, 42–44) (SI Appendix, SI Materials and Methods, Fig. S4, and Table S7 for details) in addition to the baseline. The GDP scenario incorporates economic damages due to climate change, using the nonlinear relationship found by Burke et al. (19) as a best estimate. The net economic scenario is the economic parallel to the net natural and includes carbon intensity responses and GDP responses (i.e., effects on the Kaya factors $G/P$ and $F/E$, which influence emissions in opposite directions as temperature increases) but excludes our independent estimates of population and energy intensity responses because these may be subsumed into GDP damages. Finally, our fully coupled scenario combines the net natural and net economic scenarios to include both economic and natural carbon–climate feedbacks on top of the baseline scenario.

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant DGE-1321846. J.T.R. and S.J.D. received support from NASA’s Interdisciplinary Science Program. J.T.R. received additional support from NASA’s Carbon Monitoring System (CMS) Program; the Gordon and Betty Moore Foundation (GBMF 3269); and the Reducing Uncertainty in Biogeochemical Interactions Through Synthesis and Computation (RUBISCO) Science Focus Area in US Department of Energy’s Office of Science, Division of Biological and Environmental Research.