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    Managing power demand from air conditioning benefits solar pv in India scenarios for 2040
    (Basel : MDPI, 2020) Ershad, Ahmad Murtaza; Pietzcker, Robert; Ueckerdt, Falko; Luderer, Gunnar
    An Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33-45% (without DSM) to ∼45-60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply. © 2020 by the authors.
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    Future changes in consumption: The income effect on greenhouse gas emissions
    (Amsterdam [u.a.] : Elsevier Science, 2021) Bjelle, Eivind Lekve; Wiebe, Kirsten S.; Többen, Johannes; Tisserant, Alexandre; Ivanova, Diana; Vita, Gibran; Wood, Richard
    The scale and patterns of household consumption are important determinants of environmental impacts. Whilst affluence has been shown to have a strong correlation with environmental impact, they do not necessarily grow at the same rate. Given the apparent contradiction between the sustainable development goals of economic growth and environmental protection, it is important to understand the effect of rising affluence and concurrent changing consumption patterns on future environmental impacts. Here we develop an econometric demand model based on the data available from a global multiregional input-output dataset. We model future household consumption following scenarios of population and GDP growth for 49 individual regions. The greenhouse gas (GHG) emissions resulting from the future household demand is then explored both with and without consideration of the change in expenditure over time on different consumption categories. Compared to a baseline scenario where final demand grows in line with the 2011 average consumption pattern up until 2030, we find that changing consumer preferences with increasing affluence has a small negative effect on global cumulative GHG emissions. The differences are more profound on both a regional and a product level. For the demand model scenario, we find the largest decrease in GHG emissions for the BRICS and other developing countries, while emissions in North America and the EU remain unchanged. Decreased spending and resulting emissions on food are cancelled out by increased spending and emissions on transportation. Despite relatively small global differences between the scenarios, the regional and sectoral wedges indicate that there is a large untapped potential in environmental policies and lifestyle changes that can complement the technological transition towards a low-emitting society.
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    Measuring and monitoring urban impacts on climate change from space
    (Basel : MDPI, 2020) Milesi, Cristina; Churkina, Galina
    As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations.
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    Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment
    (Basel : MDPI AG, 2022) Haselhoff, Timo; Braun, Tobias; Hornberg, Jonas; Lawrence, Bryce T.; Ahmed, Salman; Gruehn, Dietwald; Moebus, Susanne
    As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time-frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.
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    Tightening EU ETS targets in line with the European Green Deal: Impacts on the decarbonization of the EU power sector
    (Amsterdam : Elsevier Science, 2021) Pietzcker, Robert C.; Osorio, Sebastian; Rodrigues, Renato
    The EU Green Deal calls for climate neutrality by 2050 and emission reductions of 50–55% in 2030 in comparison to 1990. Achieving these reductions requires a substantial tightening of the regulations of the EU emissions trading system (EU ETS). This paper explores how the power sector would have to change in reaction to a tighter EU ETS target, and analyses the technological and economic implications. To cover the major ETS sectors, we combine a detailed power sector model with a marginal-abatement cost curve representation of industry emission abatement. We find that tightening the target would speed up the transformation by 3–17 years for different parts of the electricity system, with renewables contributing 74% of the electricity in 2030, EU-wide coal use almost completely phased-out by 2030 instead of 2045, and zero electricity generation emissions reached by 2040. Carbon prices within the EU ETS would more than triple to 129€/tCO2 in 2030, reducing cumulated power sector emissions from 2017 to 2057 by 54% compared to a scenario with the current target. This transformation would come at limited costs: total discounted power system costs would only increase by 5%. We test our findings against a number of sensitivities: an increased electricity demand, which might arise from sector coupling, increases deployment of wind and solar and prolongs gas usage. Not allowing transmission expansion beyond 2020 levels shifts investments from wind to PV, hydrogen and batteries, and increases total system costs by 3%. Finally, the unavailability of fossil carbon capture and storage (CCS) or further nuclear investments does not impact results. Unavailability of bioenergy-based CCS (BECCS) has a visible impact (18% increase) on cumulated power sector emissions, thus shifting more of the mitigation burden to the industry sector, but does not increase electricity prices or total system costs (<1% increase). © 2021 The Authors
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    System complexity and policy integration challenges: The Brazilian Energy- Water-Food Nexus
    (Amsterdam [u.a.] : Elsevier Science, 2019) Mercure, J.-F.; Paim, M.A.; Bocquillon, P.; Lindner, S.; Salas, P.; Martinelli, P.; Berchin, I.I.; de Andrade Guerra, J.B.S.O; Derani, C.; de Albuquerque Junior, C.L.; Ribeiro, J.M.P.; Knobloch, F.; Pollitt, H.; Edwards, N.R.; Holden, P.B.; Foley, A.; Schaphoff, S.; Faraco, R.A.; Vinuales, J.E.
    The Energy-Water-Food Nexus is one of the most complex sustainability challenges faced by the world. This is particularly true in Brazil, where insufficiently understood interactions within the Nexus are contributing to large-scale deforestation and land-use change, water and energy scarcity, and increased vulnerability to climate change. The reason is a combination of global environmental change and global economic change, putting unprecedented pressures on the Brazilian environment and ecosystems. In this paper, we identify and discuss the main Nexus challenges faced by Brazil across sectors (e.g. energy, agriculture, water) and scales (e.g. federal, state, municipal). We use four case studies to explore all nodes of the Nexus. For each, we analyse data from economic and biophysical modelling sources in combination with an overview of the legislative and policy landscape, in order to identify governance shortcomings in the context of growing challenges. We analyse the complex interdependence of developments at the global and local (Brazilian) levels, highlighting the impact of global environmental and economic change on Brazil and, conversely, that of developments in Brazil for other countries and the world. We conclude that there is a need to adjust the scientific approach to these challenges as an enabling condition for stronger science-policy bridges for sustainability policy-making. © 2019 The Author(s)
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    Correlated power time series of individual wind turbines: A data driven model approach
    (Woodbury, NY : American Inst. of Physics, 2020) Braun, Tobias; Waechter, Matthias; Peinke, Joachim; Guhr, Thomas
    Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function. © 2020 Author(s).
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    Balancing Health, Economy and Climate Risk in a Multi-Crisis
    (Basel : MDPI, 2021) Nathwani, Jatin; Lind, Niels; Renn, Ortwin; Schellnhuber, Hans Joachim
    In the presence of a global pandemic (COVID-19), the relentless pressure on global decision-makers is to ensure a balancing of health (reduce mortality impacts), economic goals (income for livelihood sustenance), and environmental sustainability (stabilize GHG emissions long term). The global energy supply system is a dominant contributor to the GHG burden and deeply embedded in the economy with its current share of 85%, use of fossil fuels has remained unchanged over 3 decades. A unique approach is presented to harmonizing the goals of human safety, economic development, and climate risk, respectively, through an operational tool that provides clear guidance to decision-makers in support of policy interventions for decarbonization. Improving climate change performance as an integral part of meeting human development goals allows the achievement of a country’s environmental, social, and economic well-being to be tracked and monitored. A primary contribution of this paper is to allow a transparent accounting of national performance highlighting the goals of enhancing human safety in concert with mitigation of climate risks. A measure of a country’s overall performance, combined as the Development and Climate Change Performance Index (DCI), is derived from two standardized indexes, the development index H and the Climate Change Performance Index CCPI. Data are analyzed for 55 countries comprising 65 percent of the world’s population. Through active management and monitoring, the proposed DCI can illustrate national performance to highlight a country’s current standing, rates of improvement over time, and a historical profile of progress of nations by bringing climate risk mitigation and economic well-being into better alignment.
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    Multi-temporal analysis of forest fire probability using socio-economic and environmental variables
    (Basel : MDPI, 2019) Kim, Sea Jin; Lim, Chul-Hee; Kim, Gang Sun; Lee, Jongyeol; Geiger, Tobias; Rahmati, Omid; Son, Yowhan; Lee, Woo-Kyun
    As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.
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    Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China
    ([London] : Nature Publishing Group UK, 2021) Xing, Xiaofan; Wang, Rong; Bauer, Nico; Ciais, Philippe; Cao, Junji; Chen, Jianmin; Tang, Xu; Wang, Lin; Yang, Xin; Boucher, Olivier; Goll, Daniel; Peñuelas, Josep; Janssens, Ivan A.; Balkanski, Yves; Clark, James; Ma, Jianmin; Pan, Bo; Zhang, Shicheng; Ye, Xingnan; Wang, Yutao; Li, Qing; Luo, Gang; Shen, Guofeng; Li, Wei; Yang, Yechen; Xu, Siqing
    As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.