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Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer's optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer's capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially. [Display omitted] • We model the optimal quantity of solar panels to purchase and install for consumers. • Optimal quantity increases with consumption, but the rate of increase is decreasing. • Optimal quantity increases with energy daytime cost and sell-back rate. • When solar yield is high, the impact of payment coefficient is significant. • Increased variability in consumption, optimal quantity increases. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

Degrowth and post-growth economics has emerged as a particularly fruitful approach in the debates about the reorientation of economies in the Global North towards environmental sustainability, equality, need satisfaction and democracy. This perspective promotes a planned reduction of energy and resource use in the Global North to limit environmental pressures and global inequalities and improving well-being. Yet, the specifics of this "design" are not precisely delineated. On the one hand, there is a wide acceptance, at the abstract, most general, even definitional level, that degrowth involves planning or amounts to a planned transition. On the other hand, there is strikingly little explicit engagement with, debate on, and research into what exactly "planning beyond growth" could look like. This gap urgently needs to be addressed. By exploring the degrowth-planning nexus, this paper seeks to lay a foundation for this effort. First, it identifies in the degrowth/postgrowth literature the obstacles and the opportunities for further engagement with planning. Second, it advances an agenda-setting framework, delineating problems relative to democratic ecological planning beyond growth along three axis: elaboration, implementation and multilevel dynamics. • Laying the ground for an engagement between research on democratic planning and degrowth/postgrowth. • Identifying the obstacles to a more substantial engagement with debates on planning in the degrowth/postgrowth literature. • Delineating the key requirements and challenges for democratic planning beyond growth. • Advancing a bridging framework from the current economic institutional setting towards planning beyond growth. [ABSTRACT FROM AUTHOR]

This study examines the nexus between financial expenditures within the Chinese oil business, the volatility of oil prices, and the energy-generating sector. This research examines Chinese oil and energy industry statistics from 1980 to 2020, drawing upon resource curse and energy transition theories. The results of this study are generated by using correlation analysis, quantile process estimation, autoregressive distributed lag (ARDL), and sensitivity analysis. The nexus among variables is seen across all quantiles, ranging from 22% to 81%. On the contrary, a negative association exists between oil price volatility and industrial financial expenditures, with the magnitude of the correlation ranging from 19% to 90%. Significantly, these connections exhibit statistical significance in short-term and long-term scenarios. This paper proposes implications to enhance financial expenditures in the Chinese oil business. These strategies include diversification, hedging via futures contracts and swaps, and short-term forecasts of oil price movements. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]