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In this paper, we present a newly developed eXplainable artificial intelligence (XAI) model to analyze the impacts of climate change on the cooling energy consumption (E c) in buildings, predict long-term E c under the new shared socioeconomic pathway (SSP) climate change scenarios, and explain the underlying reasons behind the predictions. Such analyses and future predictions are imperative to allow decision-makers and stakeholders to accomplish climate-resilient and sustainable development goals by leveraging the power of meaningful and trustworthy projections and insights. We demonstrated that the XAI is capable of predicting the E c under future climate scenarios with high accuracy ( R 2 > 0. 9) and reveals the critical inflection points of the daily average outdoor air temperature (T a) beyond which the E c increase exponentially. We applied the XAI model for residential and commercial buildings in hot–humid and mixed–humid climate regions to quantify the incremental impacts of climate change on E c under the different SSPs. The XAI-based analysis concluded positive and persistent incremental changes in the E c from 2020 to 2100 under all future SSP scenarios, with the maximum incremental impact of 24.5%, 33.3%, 57.8%, and 87.2% in hot–humid and 37.1%, 47.5%, 85.3%, and 121% in mixed–humid climate regions under the sustainable green energy (SSP126), business-as-usual (SSP245), challenges to adaptation (SSP370), and increased reliance on fossil fuels (SSP585) scenarios, respectively. Potential increases in the E c in future climates could have significant adverse impacts on the local and regional economy if necessary adaptation and mitigation measures are not implemented a priori. • XAI predicts building cooling energy demand under climate change. • XAI reveals the critical reason for increase in cooling energy consumption. • Space cooling energy demand is predicted to increase exponentially after 2050. • Climate change impacts on cooling energy needs are worse in residential buildings. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases and global warming are the major environmental concerns that has affected the normal geochemical cycles and climate conditions on the earth. However, a controlled production, conversion and utilization of these gases for energy generation in day to day activities of human beings can solve the problem of climate change effectively. Biogas has been recognized as a source of renewable energy available to considerably accelerate the socio-economic development. It is a multifaceted fuel containing methane, carbon dioxide and water vapors with multiple utilization options. Biogas can be valorized either directly or indirectly for various applications like the production of value-added chemicals or as a replacement of natural gas. This review article provides an in-depth analysis of various biogas valorization methods (physical, chemical and biological) for its efficient and eco-friendly utilization. The review intends to fill-up the knowledge gap through critically reviewing biogas cleaning and upgrading methods for biomethane and bio-CO 2 production for various applications including power plants, transport and value-added chemicals. The review article also highlights the conversion of biogas into fine chemicals and products of commercial importance. Chemical, biological and hybrid systems of biogas utilization are described in detail. The opportunities and impediments associated with the valorization of biogas, biomethane and bio-CO 2 have also been discussed. The paper highlights that most of the approaches to the implementation of biogas are in their infancy stage. It also identifies the bottlenecks, policies and future research and developments that are still needed in this relatively emerging field. Image 1 • Biogas is a multifaceted fuel with multiple utilization options. • Direct and indirect biogas valorization avenues. • Critical review of biogas cleaning and upgrading methods. • Conversion of biogas into value added products. • Role of microbes in biogas valorization. [ABSTRACT FROM AUTHOR]

The present paper focuses on green defaults as demand-side policies supporting the uptake of renewable energy in Germany. It sets out to gain a better understanding of whether and for whom green electricity defaults work. The present study is one of the first to use a large-scale data set to investigate this question. We combine micro-level data from the German Socio-Economic Panel (GSOEP) covering private households (including a wealth of individual information) with macro-level information such as population density of a region and proportion of energy suppliers in a given region that use a green opt-out tariff within their basic supply. We show that in Germany, green defaults, automatically enrolling customers in renewable energy sources, tend to stick, especially but not only among those who are concerned about the problem of climate change. This finding, based on real-world rather than experimental evidence, attests to the power of automatic enrollment in addressing environmental problems in Germany and potentially beyond, including climate change, and also adds to the growing literature on the substantial effects of shifting from opt-in to opt-out strategies. [ABSTRACT FROM AUTHOR]

This paper analyzes the cross-quantile dependence and causality between non-ferrous metals and clean energy indices by employing data from November 2003 to May 2019. Specifically, we utilize the time-varying copulas to examine the asymmetric connectedness among the assets. Based on the assessed dependence, we utilize the time-static and time-varying cross-quantilogram approach to evaluate the asymmetric dependence across different quantiles. Finally, we employ a Granger-causality in quantiles analysis to assess the causal relationship across different quantiles of the return distributions of the underlying assets. By utilizing time-varying copulas, we report that the conditional dependence between the assets is time-varying and asymmetric with the potential for tail dependence. Our results from the cross-quantilogram analysis provide further evidence that the interconnectedness is asymmetric across quantiles, and it increases with the increase in lags. In addition, we report that extreme market conditions positively influence the dependence structure. Finally, our findings from Granger-causality in quantiles indicate bidirectional causality among assets that intensifies with the increase in lag order. These findings are important for governmental policies that aim at mitigating the impact of climate change by transforming the global energy landscape towards clean and renewable energy sources. • Assets considered are non-ferrous metals and clean energy indices. • Time-varying copulas, Cross-Quantile dependence and causality in quantiles applied. • The assets are time-varying and asymmetric having tail dependence. • Bidirectional causality established which intensifies for higher lag orders. • Non-ferrous metals are essential for the development of clean energy assets. [ABSTRACT FROM AUTHOR]

Renewable energies are deployed worldwide to mitigate climate change and push power systems towards sustainability. Nevertheless, the weather-dependent nature and variability of renewable energy sources often hinders their integration to national grids. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. This paper introduces a novel method for quantifying total temporal energetic complementarity between three different variable renewable sources, based on well-known mathematical techniques: correlation coefficients and compromise programming. It has the major advantage of allowing the simultaneous assessment of partial and total complementarity, as well as allowing for a linear assessment of complementarity. The method is employed to study the complementarity of wind, solar and hydropower generation on different temporal scales in a region of Poland. Results show that timescale selection has a determinant impact on the estimated value of the total temporal complementarity index. • A method for simultaneous evaluation of partial and total energetic complementarity. • Method combines known metrics: compromise programming and correlations. • Timescale has a direct impact on the value of the complementarity index. [ABSTRACT FROM AUTHOR]

Biomass power is one of the most important renewable energy sources in China. In order to provide a reference for China's biomass power planning, this paper builds a power sector-planning model using the Long-range Energy Alternatives Planning System (LEAP). The results show that in the base scenario, the installed capacity of agricultural and forestry residues, municipal solid waste and biogas will increase to 22350 MW, 21150 MW, and 4900 MW, respectively by 2030. From the point of view of total volume, biomass supply is not a constraining factor for biomass power source. However, there are some social and economic factors that impede the development of the biomass power industry, some of which may not be addressed in the short term. Therefore, the development of the biomass power industry in China is a long-term process. Some policy suggestions were proposed, including reasonable planning and more subsidies for biomass supply value chain. • An optimal power-planning model focused on bio-energy in China was developed. • Agricultural and forestry power, waste power and biogas power were all analyzed. • Social and economic factors that impede biomass power were investigated. [ABSTRACT FROM AUTHOR]