• Energy Research

Air Conditioning (AC) appliances are a highly effective adaptation strategy to rising temperatures, thus making future climate conditions an important driver of space cooling energy demand. The main goal of this study is to assess the impacts of climate change on Cooling Degree Days computed with wet-bulb temperature (CDDwb) and household space cooling demand in Brazil. We compare the needs under three specific warming levels (SWLs) scenarios (1.5 °C, 2 °C and 4 °C) to a baseline with historically observed meteorological parameters by combining CDDwb projections with an end-use model to evaluate the energy requirements of air conditioning. The effects of the climate change were isolated, and no future expansion in AC ownership considered. Carbon dioxide (CO2) emissions associated with AC energy demand are also calculated. Results show an increase in both average CDDwb and AC electricity consumption for the global warming scenarios in all Brazilian regions. The Northern region shows the highest increase in CDDwb (187% in CDDwb for SWL 4 °C), while the Southeast presents the highest AC energy consumption response (326% in the AC energy consumption for SWL 4 °C) compared to the baseline. At the national level, CDDwb and the AC energy consumption in all SWLs scenarios grow by 70%, 99% and 190%, respectively.

Air Conditioning (AC) appliances are a highly effective adaptation strategy to rising temperatures, thus making future climate conditions an important driver of space cooling energy demand. The main goal of this study is to assess the impacts of climate change on Cooling Degree Days computed with wet-bulb temperature (CDDwb) and household space cooling demand in Brazil. We compare the needs under three specific warming levels (SWLs) scenarios (1.5 °C, 2 °C and 4 °C) to a baseline with historically observed meteorological parameters by combining CDDwb projections with an end-use model to evaluate the energy requirements of air conditioning. The effects of the climate change were isolated, and no future expansion in AC ownership considered. Carbon dioxide (CO2) emissions associated with AC energy demand are also calculated. Results show an increase in both average CDDwb and AC electricity consumption for the global warming scenarios in all Brazilian regions. The Northern region shows the highest increase in CDDwb (187% in CDDwb for SWL 4 °C), while the Southeast presents the highest AC energy consumption response (326% in the AC energy consumption for SWL 4 °C) compared to the baseline. At the national level, CDDwb and the AC energy consumption in all SWLs scenarios grow by 70%, 99% and 190%, respectively.

Abstract This article analyses the increase in water withdrawal due to the implementation of carbon capture by chemical absorption, with the use of monoethanolamines (MEA), in coal-fired thermal power plants installed or planned to operate in Brazil. Due to the installation of an amine-based carbon capture system, results indicate that water withdrawal increases, respectively, by 102–121% and by 93–113% in thermal power plants equipped with once-trough cooling and closed cooling systems. Therefore, if thermal power plants are installed in hydrographic regions where water levels are critical, the lack of available water may limit the implementation of CO 2 capture. The analyses show that the thermal power plants in the Candiota and Porto de Pecem regions are the most vulnerable from the viewpoint of water stress. Our findings emphasize the importance of considering water availability in deciding the places where future coal-fired power plants will be installed, especially if the objective is to build capture-ready thermal power plants.

Abstract This article analyses the increase in water withdrawal due to the implementation of carbon capture by chemical absorption, with the use of monoethanolamines (MEA), in coal-fired thermal power plants installed or planned to operate in Brazil. Due to the installation of an amine-based carbon capture system, results indicate that water withdrawal increases, respectively, by 102–121% and by 93–113% in thermal power plants equipped with once-trough cooling and closed cooling systems. Therefore, if thermal power plants are installed in hydrographic regions where water levels are critical, the lack of available water may limit the implementation of CO 2 capture. The analyses show that the thermal power plants in the Candiota and Porto de Pecem regions are the most vulnerable from the viewpoint of water stress. Our findings emphasize the importance of considering water availability in deciding the places where future coal-fired power plants will be installed, especially if the objective is to build capture-ready thermal power plants.

Abstract Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

Abstract Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.