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In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

Transitioning to sustainable energy is a global imperative for countries to develop policies to achieve Sustainable Development Goals (SDGs), particularly SDG 7 and 13. Given the possible association between digital inclusion (DIGI) and the energy transition (EGTN), we examined the impact of DIGI on the EGTN while controlling for environmental taxes (ENTX) in the top-5 greenest economies in the world using quarterly data from 2001 to 2021. We examined the association at short-, medium-, and long-term frequencies using comprehensive approaches such as the Method of Moments Quantile Regression (MMQR) for panel analysis and Wavelet techniques, i.e., wavelet coherence, multiple wavelet, and partial wavelet for country-specific assessment. According to the MMQR results, DIGI has a significant and positive effect on EGTN, with a coefficient value ranging from 10.95 to 5.980 while moving from lower (0.25) to higher (0.95) quantiles. Similarly, ENTX has a positive and significant effect on EGTN, with a coefficient value ranging from 1.770 to 3.132 as the quantiles increase. In addition, the results of wavelet coherence expose a strong and positive connectedness between DIGI and EGTN in each sample country. Furthermore, multiple wavelet results for each country demonstrated that ENTX significantly strengthens the interaction between DIGI and EGTN. Our findings result in developing SDGs-oriented policy guidelines, specifically the policy strategy for addressing SDG-7 and SDG-13. The study endorses that the sample countries take initiatives to accelerate the EGTN process by improving digital inclusion and implementing an effective environmental taxation system. • We evaluated the role of digital inclusion on energy transition. • The study also considers the role of environmental taxes. • The study employed panel data from 2001 to 2020 for the greenest economies. • Digital inclusion and environmental taxes promote energy transition. • The panel results are validated by the wavelet analysis. [ABSTRACT FROM AUTHOR]

Despite having multidimensional positive externalities of renewable energy (RE) in theory, the literature on RE's economic and environmental determinants is still very nascent. We have addressed this research gap by considering a balanced panel of 38 OECD nations attempting a phase-by-phase energy transition. We have used data spanning from 2000 to 2020 and deployed the novel method of moments quantile regression (MMQR) approach. Our results unveil that economic complexity reduces the consumption of renewable energy among the OECD nations, indicating that as the export becomes diversified, energy consumption at the production level becomes more dependent on non-renewable sources. This further suggests that OECD nations are yet to meet their renewable energy generation threshold. Moreover, energy efficiency fosters the consumption of renewable energy, substantiating that energy efficiency and renewable energy share complementary co-movement among the OECD nations. Furthermore, we unravel that environmental technology and natural resource rent have heterogeneous effects on renewable energy consumption in OECD. For instance, environmental technology only boosts RE consumption at the higher quantiles (70th to 95th), whereas revenue from natural resource rent curbs RE consumption. Our conclusions hold after a series of robustness checks. Overall, we propose that OECD nations should only export those products with absolute comparative advantage to reduce excessive dependence on fossils. Additionally, investment in renewable energy technology development should be escalated to ensure energy efficiency in the long-term. • The impacts of economic complexity (ECI), environmental technology (ENT), natural resource rent (NRT) and energy efficiency (EFX) on green energy use explored. • We use novel non-parametric method of moments quantile regression approach for OECD countries. • The ECI curbs the consumption of renewable energy while EFX is a boon for it. • ENT and NRT have heterogenous effects on RE consumption. • We also note different causal relationships to rectify the results' robustness. [ABSTRACT FROM AUTHOR]

Water Distribution Networks (WDNs) represent a noteworthy field for possible implementation of Small Hydropower (SHP), by replacing Pressure Reduction Valves (PRV) with turbomachines, in particular Pump as Turbines (PaTs), to control and regulate the pressure, while harvesting energy otherwise wasted. Different models were developed to predict the performance and select the positioning of the PaTs for the maximum energy recovery but most of them neglect practical aspect such as: power grid limitations and optimal harvesting strategy. In this framework, we intend to propose a new method to select a PaT, defining its optimal working point, by introducing an energy exploitation coefficient. The proposed methodology is based on the experimental results of a real PaT tested in the high capacity hydraulic laboratory at Polytechnic University of Bari. Firstly, the selected commercial centrifugal pump was tested in both pump and turbine modes. Then, three different approaches, for the Best Efficiency Point (BEP) selection, are described and compared in terms of energy exploitation and capacity factor for a WDN. The first consists of selecting the BEP at the average flow rate, the second one considers the probability distribution of the flow rate and the corresponding available hydraulic energy, whereas the latter is based on the highest energy harvesting. By applying energy production, economic and environmental analyses, the new proposed methodology, based on the third approach, shows a remarkable advantage in terms of exploited energy. Indeed a remarkable 60% energy recovery is achieved with 334 ton CO 2 /year avoided. Furthermore, the impact of the electrical motor on the maximum power generation (cut-off) is considered. Eventually, useful insights for the future PaT selection and installation are discussed. • Characterization of a PaT selected to work at the mean flow rate of a WDN. • Two new approaches are proposed for PaT selection, given real data of PaT and WDN. • Methods comparison: mean flow rate, max available energy and max energy recovery. • Runaway and blocked rotor curves, affinity laws and technical needs are considered. • Over 3.5 MWh/day, a 60% energy recovery is achieved with 334 ton CO 2 /year avoided. [ABSTRACT FROM AUTHOR]

• Novel data-driven platform to predict and evaluate energy transition policies. • Quantitatively evaluate impacts of energy transition policies. • Make effective forecasting and timely adjustment in energy policies. • Powerfulness of platform is demonstrated in representative metropolitan regions. • Adjustment recommendations for different stakeholders in energy industry. To meet widely recognised carbon neutrality targets, over the last decade metropolitan regions around the world have implemented policies to promote the generation and use of sustainable energy. Nevertheless, there is an availability gap in formulating and evaluating these policies in a timely manner, since sustainable energy capacity and generation are dynamically determined by various factors along dimensions based on local economic prosperity and societal green ambitions. We develop a novel data-driven platform to predict and evaluate energy transition policies by applying an artificial neural network and a technology diffusion model. Using Singapore, London, and California as case studies of metropolitan regions at distinctive stages of energy transition, we show that in addition to forecasting renewable energy generation and capacity, the platform is particularly powerful in formulating future policy scenarios. We recommend global application of the proposed methodology to future sustainable energy transition in smart regions. [ABSTRACT FROM AUTHOR]

In order to analyse international effects of national energy policies, we investigate the spill-over effects of the German Energiewende on the Dutch power market, which is closely connected to the German market. We estimate the impact of the German supply of wind and solar electricity on the Dutch day-ahead price of electricity and the utilisation of the conventional power plants. We take cross-border capacity constraints into account and use hourly plant-level data over 2006–2014. We find that the price elasticity of German wind on Dutch day-ahead prices is −0.03. However, this effect vanishes when the cross-border capacity is fully utilised. We find a modest negative impact on the utilisation of the Dutch power plants. As such, we conclude that the German Energiewende has had modest spill-over effects to the Dutch market. The recent dramatic performance of the Dutch gas-fired plants can be attributed to the changes in the relative prices of coal versus natural gas. We conclude that national energy policies in one country do not necessarily strongly affect neighbouring markets in case of constrained cross-border capacities. [ABSTRACT FROM AUTHOR]

• Four environmental indicators suitable for use in energy system models are proposed (85) • The indicators allow direct comparison of decarbonisation potential of renewables (84) • Gearbox double-fed induction generators are the preferred form of wind turbine (80) • Cadmium telluride photovoltaics exhibit best overall raw materials outcomes (78) Raw materials and their related environmental impacts will play a key role in the implementation of renewable energy infrastructures for decarbonization. Despite the growing amount of data quantifying raw materials for energy production technologies, few examples of these data sources are being included in current energy system models. Accordingly, this paper introduces possible pathways for integrating material-specific life cycle assessment outputs and material metabolism indicators into energy system models so that raw material requirements, and their associated impacts, can be accounted for. The paper discusses the availability of life cycle inventories, impact assessment methods and important output indicators. The material metabolism indicators most relevant to the current policy debate surrounding the European Green Deal–namely, material supply risk and contribution of recycled materials to total supply–are also discussed alongside the value of adding this information to energy system models. A methodology for using data from both approaches is offered and operationalised using four sub-technologies of both wind turbines and solar photovoltaic panels as case studies. The results show that considerable variation exists between and within the two groups for all indicators. The technologies with the lowest global warming potential, cumulative energy demand and supply risk are turbines with gearbox double-fed induction generators and cadmium telluride photovoltaics. Furthermore, wind turbines exhibit significantly higher recycling rates than photovoltaics. Ultimately, the integration of such methodologies into energy system models could greatly increase the awareness of raw material issues and guide policies that maximise compatibilities between resource availability and cleaner energy systems. [ABSTRACT FROM AUTHOR]

During the necessary transition to sustainable power systems, fossil generators are used to complement generation from renewable energies. Yet not all fossil technologies can be operated flexibly enough to balance the fluctuating output of renewables. I study how the efficient transition to renewables is influenced by the endowment with flexible and inflexible generators. To this end, I develop a parsimonious analytical model that incorporates a variable renewable energy (VRE) technology with stochastic generation availability as well as flexible and inflexible fossil generators with rigid capacities. I find that early in the transition, VRE generation replaces generation from flexible capacities. Only later is the output of inflexible generation reduced and substituted by increasing flexible and renewable generation. The early deployment of VRE is hampered by high inflexible capacities. However, deployment speed surges once VRE generation begins to substitute inflexible generation. The more excess capacities exist in the initial endowment, the higher the sudden increase in deployment is. The decreasing use of inflexible fossil generation is usually accompanied by an increasing utilization of flexible generators. Nevertheless, constructing additional flexible capacities is only cost-efficient if a cost threshold is not exceeded. By contributing to a better understanding of the impact of flexibility on efficient VRE deployment, this work aims to facilitate an efficient transition to renewable energies. • New analytical model to analyze power system transitions under limited flexibility. • Renewables first replace flexible generation and later inflexible generation. • Instant increase of renewables is efficient once use of inflexible plants decreases. • Benefits of flexible generation peak when inflexible plants are no longer used. • Excess capacity endowments induce fluctuations in renewable deployment. [ABSTRACT FROM AUTHOR]