11 Evaluation of Climate Change: Strategies : Simple and Complex

11 Tools to Evaluate Mitigation Strategies

The previous blogs summarize the massive amount of literature on climate change. As stated in the first climate change blog the devil is in the details and the details are the strategies to implement a zero CO2 strategy. This blog discusses the tools used to evaluate zero CO2 strategies.

All economic activity requires energy; to the extent this energy comes from fossil fuels, the energy use results in emissions of carbon dioxide, CO2. The major CO2 emitting countries are the world’s largest economies. The nature of this link between the growth in economic activity and carbon emissions is a critical question for climate change. Linkage implies that deep emission reductions will constrain economic growth; decoupling implies that deep emission reductions are possible with little or no effect on growth. The most instructive tool for analyzing this ‘‘irreversible trend’’ and ‘‘decoupling’’ is the Kaya identity, which establishes an ironclad connection between emissions and economic growth. (Kaya, Y., and Yokoburi, K. (1997). Environment, Energy, and Economy: Strategies for Sustainability (United Nations University Press), ISBN 9280809113.) The Kaya identity identifies the driving forces of CO2 emission. The identity is used to project CO2 emissions based on these drivers. To understand the environmental impact of emissions an integrated model is used to calculate values for variables such as global temperature.

11.1 CO2 Emission Model

Emission decomposition analysis is the principle tool used to understand these driving forces. (Decomposition method or analysis is a generic term for solutions of various problems and design of algorithms in which the basic idea is to decompose the problem into subproblems.) The emissions decomposition analysis consists of an accounting identity. The identity decomposes emissions into component indicators, in order to describe the driving forces of emissions in a given inventory. The resulting accounting identity can be multiplicative or additive. The Kaya identity decomposes emissions multiplicatively into population, income, energy intensity and carbon intensity:

CO2=P*(G/P)*(E/G)*(F/E)

where:

• CO2 is global CO2 emissions from human sources (MMtonnesCO2eq)

• P is global population (MM)

• G is world GDP (MM$)

• E is global energy consumption (MMtonnesOileq) or TetraWattHours

and

• G/P is the GDP per capita ($)

• E/G is the energy intensity of the economy (TetraWattHours/$), EI

• F/E is the carbon intensity of energy (MMtonnesCO2eq/ TetraWattHours), CI

or

CO2 = population * income per capita * EI * CI

The usefulness of the Kaya decomposition is illustrated by the following decomposition of emission data for the US and China.

Throughout the period 1990-2018, CO2 emissions are strongly coupled to GDP for China. Prior to 2000 the US CO2 emissions were also coupled to GDP but starting in 2001 they are decoupled. The US CO2 emissions after 2001 are most strongly coupled with CI (CO2 Intensity).

The components of the Kaya identity are reported in the EIA IEO (https://www.eia.gov/outlooks/ieo/), the Enerdata Global Statistical Yearbook (https://yearbook.enerdata.net/) and other sources including the BP Statistical Review of World Energy https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.htmlhttps://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html, the UN World Population Prospects (https://population.un.org/wpp/), IMF World Economic Outlook (https://www.imf.org/en/Publications/WEO).

11.2 Global Temperature Model

Integrated Science Assessment Model (ISAM) was developed to make it possible for policy makers, educators, students of climate and climate change, and instructors to use the Integrated Science Assessment Model (ISAM)over the web (http://climate.atmos.uiuc.edu/isam2/index.html).

ISAM is a state-of-the-art model that takes projections of human emissions of CO2 and other greenhouse gases and of atmospheric particulates and generates predictions of future greenhouse gas and aerosol concentrations, global climate change, and the impacts of climate change such as the expected rise in sea level.

The goal of this site is to integrate the human user into a climate modeling system, and allow the user to:

• develop scenarios of greenhouse gas and aerosol emissions

• evaluate the impact of anthropogenic emissions on the global climate and on sea level

• observe the direct and tangible results of policy decisions regarding energy consumption, choices of energy sources, and agricultural and land-use practices

• alter the various physical formulations of ISAM and examine the impact of such changes on the climate system learn about the implications of various structures of the modeled system

• assess the role of climate sensitivity in global climate response

ISAM for assessment of climate change consists of coupled modules for representation of the carbon cycle, effects of greenhouse gas emissions and aerosols on atmospheric composition, effects on global temperatures using an energy balance model, and processes affecting sea level change. This model has been used to estimate the relation between the time-dependent rate of greenhouse gas emissions and quantitative features of climate global temperature, the rate of temperature change, and sea level that are thought to be indicators of human impact on climate and ecosystems. The concentration of CO2 is calculated by a globally averaged carbon cycle model, which consists of four reservoirs, namely the atmosphere, the terrestrial biosphere, the mixed ocean layer, and the deep ocean. The atmosphere and the mixed layers are modeled as well mixed reservoirs. The carbon cycle model in ISAM was originally developed as a tool to predict the likely future changes of the atmospheric abundance of CO2 dependent on the use of fossil fuels, deforestation and expansion of agriculture land. This schematic model for the carbon cycle is constructed to be consistent with current understanding of the global carbon cycle.

11.3 Model Testing

To analyze various mitigation scenarios, projections of future CO2 emissions are calculated using a spreadsheet of the Kaya identity, with historical data from 1990 through 2018. Projections are calculated using UN population projections and an annual rate of change for EI, CI and GDP per capita applied to the 2018 value. Future annual CO2 emission were calculated using annual projected rates of change of EI=0.7%, CI=-3.4%, and GDP per capita = 3.0%. The calculated emissions through 2018 match the EIA annual values within ~1% with an error in the projected total CO2 between 2019 and 2050 of 0.3%.

To test this Kaya prediction, CO2 emission are entered in the ISAM (Integrated Science Assessment Model) to calculate climate change parameters and results compared to existing EIA projections. This model allows entry of the concentration of C in ten-year intervals from 2000 to 2100 and a single entry for 2200. The ten-year values from the EIA IEO were used for years 2010 through 2050, an extrapolation of the 2020-2050 values were used for 2050-2100 and the 2200 value was set to zero. (The current version allows entry every ten years between 2000 and 2100) The Kaya data used the CO2 emissions based on the indices from the previous paragraph and 2020 emissions were set to zero. The following shows the close agreement of the Kaya identity model with the EIA model and the fact that global temperature change stabilizes at ~3.5 oC for this emission scenario.

The calculated sea level changes are within a centimeter in 2100.

The EIA lower economic growth case uses a 2.4% GDP rate as opposed to the 3% growth rate used in the base case. The effect of this is to increase global temperatures by about 0.2 oC.

11.3 Impact of a Warmer World Will Not Be Evenly Distributed

The EIA projects a temperature rises near 4°C . The global impact of such a temperature would have huge impacts on humanity ( Turn Down the Heat: Why a 4°C Warmer World Must Be Avoided, (https://openknowledge.worldbank.org/handle/10986/11860). Per this report:

Temperature

The effects of 4°C warming will not be evenly distributed around the world, nor would the consequences be simply an extension of those felt at 2°C warming. The largest warming will occur over land and range from 4°C to 10°C. Increases of 6°C or more in average monthly summer temperatures would be expected in large regions of the world, including the Mediterranean, North Africa, the Middle East, and the contiguous United States

Figure 35 plots the multi-model mean of the warmest July and January temperatures encountered during the period 2080–2100. The warmest July month in the Sahara and the Middle East will see temperatures as high as 45°C, or 6–7°C above the warmest July simulated for the present day. In the Mediterranean and central United States, the warmest July in the period 2080–2100 will see temperatures close to 35°C, or up to 9°C above the warmest July for the present day. Finally, in the Southern Hemisphere, record monthly summer extremes namely, January) will be as warm as 40°C in Australia, or about 5°C warmer than the most extreme present-day January. Note that temperatures presented here are monthly averages, which include night-time temperatures. Daytime temperatures can be expected to significantly exceed the monthly average.

Sea Level Rise

The difference in regional sea-level rise patterns between 4°C and 2°C warming above preindustrial temperatures is indicated in Figure A1.2 for both ice-sheet scenarios by the end of the century. In both ice-sheet scenarios, the spatially variable component of the difference is closely related to ocean dynamics (see Figure A1.3). The benefit of choosing a 2°C pathway, rather than a 4°C pathway can be to limit more than 20 cm of local sea-level rise (Figure A1.2).