ITS2.16/CL3.2.19

We propose a dynamic statistical model of the Global Carbon Budget (GCB) as represented in the annual data set made available by the Global Carbon Project (Friedlingsstein et al., 2019, Earth System Science Data 11, 1783--1838), covering the sample period 1959--2018. The model connects four main objects of interest: atmospheric CO2 concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink) and by the ocean and marine biosphere (ocean sink).  The model captures the global carbon budget equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World gross domestic product (GDP), and sink activity depends on the level of atmospheric concentrations and the Southern Oscillation Index (SOI). We use the model to determine the time series dynamics of atmospheric concentrations, to assess parameter uncertainty, to compute key variables such as the airborne fraction and sink rate, to forecast the GCB components from forecasts of World-GDP and SOI, and to conduct scenario analysis based on different possible future paths of World-GDP.

How to cite: Hillebrand, E., Bennedsen, M., and Koopman, S. J.: A Statistical Model of the Global Carbon Budget, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-826, https://doi.org/10.5194/egusphere-egu21-826, 2021.

How to cite: Margaritella, L., Friedrich, M., and Smeekes, S.: High-Dimensional Granger Causality for Climatic Attribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8020, https://doi.org/10.5194/egusphere-egu21-8020, 2021.

The following paper analyses monthly trends for CO₂ emissions from energy consumption for 31 European countries, four primary fuels (i.e., Crude Oil, Natural Gas, Hard Coal, Lignite) and three secondary fuels (i.e., Gas/Diesel Oil, LPG, Naphta, Petroleum Coke) from 2008 to 2019. Carbon dioxide emission has been estimated following the Reference Approach in the 2006 IPCC Guidelines for National Greenhouse Gasses Inventories. Country-specific (e.g. Tier 2) coefficient were retrieved from the IPCC Emission Factor Database and the UN Common Reporting Framework. Data on fuel consumption (e.g., Gross Inland Deliveries) were taken from the Eurostat database. This paper will fill some knowledge gap analysing monthly trends of carbon dioxide emissions for major EU Countries. As the progressive phase-out of carbon is taking place pretty much in all Europe, Crude Oil exerted the largest amount of carbon dioxide emissions in the period considered. Analysis of selected countries unveiled several clusters within the EU in terms of major source of emissions. As final step, the paper has endeavoured the task of fitting a model for monthly CO₂ forecasting. The whole series presents two structural breaks and can be explained by an autoregressive model of the first order. Indeed, further speculations on a more appropriate fit and more fuels in the estimation, is demanded to other works.

How to cite: Quatrosi, M.: Analysis of monthly CO2 emission trends for major EU Countries: a time series approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1132, https://doi.org/10.5194/egusphere-egu21-1132, 2021.

We propose a measure of investors’ climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors’ climate sentiment generates a mispricing in the Emission-minus-Clean (EMC) portfolio (Choi et al., 2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices are a successful incentive to reduce CO2 emissions. Finally, our model can predict the price of the EMC portfolio also for long-term horizons.

How to cite: Santi, C.: Investors’ Climate Sentiment and Financial Markets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2016, https://doi.org/10.5194/egusphere-egu21-2016, 2021.

Relevance: Extreme temperature events, both heatwaves and cold spells, can put pressure on power systems’ reliability by pushing power demand to record highs. Within the literature assessing the impacts of climate change on the energy sector,  gathering new evidence on the drivers of peak load is a pressing issue for multiple reasons. First, peaks in power load must be accommodated by exceptional ramp-up requirements of power generating units, so that in the future adapting to climate change may involve the construction of plentiful under-utilized peak generation plants, putting pressures on the decarbonization goals and increasing stranded assets risks. Furthermore, peak load shocks induced by extreme temperatures can coincide with reduced transmission and distribution capacity, further challenging the operation of electricity grids [1].

Both the empirical and modeling literature assessing the impacts of climate change on the energy sector have generally focused on aggregated electricity demand rather than on its peaks. Few available empirical studies  investigate how extreme events can affect peak demand focus on industrialized countries and estimate reduced-form models, that hold adaptation, economic growth, technology, and current infrastructure constant [2,3]. Our paper aims to fill this gap by identifying if and how climatic and socio-economic drivers can affect the magnitude of the peak load response to extreme weather events.

Methods: We assess these interrelated dynamics by exploiting high-frequency power demand data collected from load balancing authorities. Specifically, we assemble a novel dataset spanning for the last two decades across more than 100 power markets, comprising both countries (European Member States, Asian and African countries) and large sub-national regions (power markets in Japan, Australia and Russia and Federal States or Provinces in the US, Canada, Brazil and India). The dataset includes: i) daily peak and total load; ii) daily population-weighted exposure to weather from 3 hourly near surface temperature data at 0.25 degrees gridded resolution; iii) quarterly and yearly regional statistics and indicators on demography, economy, education and innovation. We investigate how daily peak load responds to extreme temperatures by adopting a suite of time-series and panel econometric methods that fully exploit the high-frequency and sub-national disaggregation of our dataset.

Results: Utilizing the innovative methodological framework proposed, we: i) identify how peak load responds to temperature extremes in different regions; ii) test if and how such response can be modulated by regional climatic and socio-economic characteristics; iii) derive cost implications due to the amplification of peak demand deriving from future increases in the intensity and frequency of extreme events.

References:

[1] Yalew, S. G., van Vliet, M. T., Gernaat, D. E., Ludwig, F., Miara, A., Park, C., ... & Van Vuuren, D. P. (2020). Nature Energy, 5(10), 794-802.

[2] Auffhammer, M., Baylis, P., & Hausman, C. H. (2017). PNAS, 114(8), 1886-1891.

[3] Wenz, L., Levermann, A., & Auffhammer, M. (2017). PNAS, 114(38), E7910-E7918.

How to cite: Colelli, F., De Cian, E., Mistry, M., and Mammi, I.: Implications of extreme temperatures and socio-economic development on power markets’ peak demand across the world, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7923, https://doi.org/10.5194/egusphere-egu21-7923, 2021.

Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.

How to cite: Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M., and Levermann, A.: Day-to-day temperature variability reduces economic growth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9973, https://doi.org/10.5194/egusphere-egu21-9973, 2021.

Weather matters at the local level. Microeconomic climate econometric analyses find evidence that weather has localized effects on labor supply, agricultural yields, mortality rates, and other socio-economic measures. However, macroeconomic analyses at the national level find no evidence that weather affects macroeconomic aggregates, such as GDP or aggregate productivity in the US and other developed economies. These results present a seeming contradiction. In this paper, I develop a general equilibrium theoretical model of an economy with localized weather shocks to bridge the gap between microeconomic and macroeconomic studies. The theoretical model provides a simple, modular framework for aggregating weather shock impacts. I apply the findings to an empirical setting in the US, a prime example of the contradictory findings. I first estimate the microeconomic impacts of weather on labor productivity growth across county-industry pairs in the US from 2002 to 2017. I then apply these to construct annual estimates of the impact of weather shocks across the economy on US GDP according to the theoretical framework. I construct confidence intervals using the estimated microeconomic impact uncertainty. Across the sample years, I find no evidence that the annual impacts are distinct from $0. I then deconstruct the aggregate impacts, again following the theoretical framework, to examine what generates this no-effect result. I find consistent evidence of statistically significant but heterogeneous effects across a majority of counties and industries. For example, within a given year, over two-thirds of counties are consistently and significantly impacted by their local weather. This effect is positive for some counties and negative for others. I show that it is the aggregation of these heterogeneous impacts across the spatial distribution and industrial composition of the economy that masks the impact of weather. This finding highlights the importance of understanding micro-level economic impacts and changes in the composition of economic activity for projections of future macroeconomic climate change impacts.

How to cite: Harding, A.: From Micro-level Weather Shocks to Macroeconomic Impacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13189, https://doi.org/10.5194/egusphere-egu21-13189, 2021.

Tourism is an essential sector of the economy of the Canary Islands. Tourism Climate Index (TCI) and Holiday Climate Index (HCI) are good indicators of environmental conditions for leisure activities. Regional climate model (RCM) has been addressed to analyze the impact of climate change on the indices of tourist areas. The initial and boundary conditions for future scenarios are prescribed through three CMIP5 models (GFDL, IPSL and MIROC)  surface and lateral boundary conditions within the Meteorological Research and Forecast (WRF), with a high resolution, 3x3 km. Two time periods (2030 – 2059, and 2070-2099) and two Representative Concentration Pathways (RCPs 4.5 and 8.5) are considered. Tourism indicators are projected to improve significantly during the winter and shoulder seasons, but will worsen in the summer months, including October, in the southeast, which is where hotels are currently located.

How to cite: Carrillo, J., González, A., Pérez, J. C., Expósito, F. J., and Díaz, J. P.: Impact of climate change on the future of tourism areas in the Canary Islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11981, https://doi.org/10.5194/egusphere-egu21-11981, 2021.

Net positive buildings can be the solution to slow down climate change. Old buildings and minimum code buildings only strive for structural protection, but they do not play a part in climate change mitigation solutions. In this study, we try to demonstrate net positive buildings' contribution in reducing global greenhouse gas emissions by taking the Guwahati region (India) as a study area. First, we developed a north-facing 3-B-H-K residential building plan with a two-car garage using the most commonly used construction materials in the region as a base case scenario. The weather data (like Temperature, Relative Humidity, and Airspeed) for 2020 is collected. With these inputs, the annual total energy consumption for the present climatic condition is simulated using the Ecotect tool. Then three different scenarios (modification of walls, modification of roofs, and floor modification) were created. The energy interpretation for the overall modified case was done and compared with the base case scenario. The result indicates that the total annual energy consumption for the overall modified case was reduced by 70% as compared to the base case model. The remaining 30% of the energy usage was supplied by renewable energy sources using photovoltaic cells to make net energy consumption zero.  These findings suggest that the old building can be renovated and modified to act as a mitigation solution to climate change.

How to cite: Bharali, B., Rajbanshi, B., Yangzom, T., Dahal, H., Rai, M., and Sewa, B.: Promoting Sustainable Housing with Fundamental Shift beyond Net-Zero and Green Building, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15833, https://doi.org/10.5194/egusphere-egu21-15833, 2021.

Integrated Assessment Model (IAM) design philosophy currently focuses on demand-side global carbon pricing as the principal policy tool to drive mitigation. However, ambitious mitigation scenarios produced with these IAMs rely heavily on the availability of carbon capture and storage (CCS) technologies in the mid-century, at the scale of billions of tonnes. If integrated assessment continues to employ demand-side policies exclusively we risk a gap forming between the requirements of economically-optimal mitigation trajectories in these IAMs and the reality of developed CCS capacity.

If CCS capacity fails to keep up with the ambition of mitigation policy, carbon prices could rise well above the cost of direct air capture as markets aim to drive residual emissions down. To avoid this, scenarios could include both supply and demand-side policies in tandem, where supply-side policies are targeted to increase CCS capacity to appropriate levels.

One such supply-side policy option is a Carbon Takeback Obligation (CTBO), where suppliers of fossil carbon are required to recapture and store an increasing fraction of the carbon in their products. This ‘stored fraction’ would be increased from near zero at present, up to 100% at the time of net-zero. By applying such a policy suppliers of fossil carbon products are forced to take responsibility for decarbonising their own products and provide the drive to develop the CCS capacity necessary to achieve net-zero emissions in the mid-century. In theory, if a CTBO was enforced globally the costs associated with the production of one tonne of CO₂ would be capped around the price for the capture, transport and storage of diffuse, mobile, or otherwise hard-to-abate CO₂ emission sources (i.e. the cost of direct air capture).

Here, we discuss the implementation of a global CTBO. Using an Integrated Assessment Model emulator, tuned to existing IAM carbon price/abatement rate relationships, we explore the total policy cost of applying a CTBO globally to achieve net-zero by 2050. Using the emulator we harmonise the combined CTBO and demand-side carbon price policies, and show how a SSP2-26 level of ambition can be achieved using these policies with a similar total policy cost. Further, we explore what additional near-term carbon prices can be included to achieve SSP2-19 level policy ambition. These results suggest there are significant benefits to defining climate policy around measures targeting suppliers of fossil carbon, including for long-term planning, implementation and governance of the policy, and overall cost. For further insight, and to provide a greater variety of policy options feeding into IPCC’s WG3, we argue IAMs should look to include CTBO-like policies in future scenario design.

How to cite: Jenkins, S., Mitchell-Larson, E., Ives, M., Haszeldine, S., and Allen, M.: Using an emulator to apply a Carbon Takeback Obligation alongside demand-side carbon pricing in Integrated Assessment Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10961, https://doi.org/10.5194/egusphere-egu21-10961, 2021.