The analysis was co-ordinated by Araceli Fernandez Pales (lead on end-use modelling and innovation), Peter Levi (lead on industry), Uwe Remme (lead on supply modelling and hydrogen) and Jacob Teter (lead on transport). The main contributors were Thibaut Abergel (co-lead on buildings, decomposition), Praveen Bains (bioenergy), Jose Miguel Bermudez Menendez (hydrogen), Chiara Delmastro (co-lead on buildings, cooling), Marine Gorner (transport), Alexandre Gouy (batteries, recycling), Raimund Malischek (fossil fuels), Hana Mandova (industry), Trevor Morgan (Menecon consulting), Leonardo Paoli (batteries, spillovers, patents), Jacopo Tattini (transport, shipping), Tiffany Vass (industry and material efficiency). Other contributors were Ekta Bibra, Till Bunsen, Elizabeth Connelly, Hiroyoki Fukui, Taku Hasegawa, Pierre Leduc, Francesco Pavan, Sadanand Wachche and Per-Anders Widell. Caroline Abettan, Claire Hilton, Reka Koczka and Diana Louis provided essential support.
Abstract As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world’s energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country’s 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China.
[Objective] Research on the collision between ships and structures such as docks and bridge piers has been relatively mature. Truss-type aquaculture cages differ significantly from docks and bridge piers in terms of structural form and overall stiffness. Therefore, further research on the collision characteristics between ships and truss-type aquaculture cages is needed. [Method] Based on ANSYS/LS-DYNA, numerical models of ships, rubber fenders and cages were established to conduct numerical simulations of ship berthing. The berthing of a 500 t maintenance ship and a 1300 t live-fish transportation ship to the cages was compared in different ways and at different speeds. [Result] The research shows that: when the 500 t maintenance ship berths in different ways, the greater the stiffness of the collision area between the ship and the cage, the lower the energy-absorption proportion of the cage and the greater the collision force. When berthing at different speeds, as the initial speed of the ship increases, the proportion of energy absorbed by the cage increases, which can reach up to 94.5%. When the 500 t maintenance ship and the 1300 t live-fish transportation ship berth in the same way, at a relatively low speed, the 1300 t live-fish transportation ship has a greater rebound speed during berthing and the cage absorbs less energy. At a relatively high speed, the cage absorbs more energy. [Conclusion] Operation and maintenance ships should dock with truss style net cages from the top, and it is recommended that the berthing speed should not exceed 1.5 m/s. Single column berthing should be avoided as much as possible, and the lateral berthing speed of live water transport ships should not exceed 1.0 m/s.
Wiryanta Muljono, Sri Setiyawati, Priyanka Pertiwi Setiawati
et al.
This study investigates the multifaceted relationship between AI-driven technological transformation and the demand for downstream petroleum products in achieving Indonesia's longterm economic growth goals, aligning with the "Golden Indonesia 2045" vision. Employing a mixedmethods approach, the research quantitatively assesses the immediate impact of AI on downstream petroleum operational efficiency (the first hypothesis) and its subsequent influence on critical macroeconomic indicators like GDP growth and the oil and gas trade balance (the second hypothesis). Concurrently, it qualitatively examines the strategic alignment of national AI policies, such as the National AI Strategy from 2020 to 2045 (Strategi Nasional Kecerdasan Artifisial, Stranas KA) and the "Making Indonesia 4.0" roadmap, with downstream energy development plans (the third hypothesis), while identifying associated implementation challenges. Findings reveal a significant positive correlation between AI adoption and improved operational efficiency within the downstream sector (supporting the first hypothesis). This is substantiated by evidence of sophisticated AI applications, including predictive maintenance (PdM) powered by advanced computational methods, which ensures continuous operation and extends the life of critical hydrocarbon assets. Furthermore, AI-integrated fuel blending systems demonstrate high precision, achieving a coefficient of determination (R 2) of 0.99 during validation, which showcases robust real-time optimization capability that surpasses traditional modeling and reduces waste. However, the analysis of macroeconomic leverage provides only partial support for the second hypothesis. While AI-influenced efficiency-by maximizing domestic output and optimizing costs-shows a statistically significant, albeit moderate, positive impact on reducing the oil and gas trade deficit and boosting GDP growth, this effect is severely limited by persistent structural issues. Specifically, petroleum imports have a large and negative impact on Indonesia's economic growth. The operational savings are currently dwarfed by the volume of necessary imports and the enormous fiscal burden imposed by incomplete fuel subsidy reforms, which peaked at 2.8% of GDP in 2022. The oil and gas trade balance persists in a deficit, recording-1.55 billion USD in May 2025 and-1.58 billion USD in July 2025, even amidst an overall national trade surplus. The study confirms a strong top-down strategic alignment between national AI and energy sector policies. Nevertheless, significant implementation hurdles highlight the necessity for targeted policy intervention (supporting the third hypothesis). These pervasive barriers include chronic infrastructure gaps, weak data governance frameworks, severe digital skills shortages Digital economy: theory and practice Цифровая экономика: теория и практика Цифровая экономика: теория и практика requiring systematic improvement from foundational education, high initial investment costs and profound organizational inertia within large enterprises, leading to a "pilot trap", where successful small-scale projects fail to scale up due to cultural and systems integration difficulties. Ultimately, these challenges temper the transformative potential of AI, shifting its current role primarily towards improving operational efficiency within the legacy system. For AI to become a driver of fundamental structural change-the necessary process of reallocating labor and resources toward higherproductivity modern industries-policy interventions must link AI investment to comprehensive energy subsidy reform and the acceleration of the new and renewable energy sector. This research bridges a critical gap in the literature by offering an integrated analysis of technology adoption in a resource-dependent emerging economy, providing evidence-based recommendations for policymakers and industry leaders to effectively leverage AI for sustainable and structural economic growth.
Abstract In response to severe energy and environmental challenges, electric vehicles (EVs) have been thoroughly considered by governments. Although China has become the largest EV market, currently the limitations of EVs and decreasing subsidies have led to a challenging future. To promote EV diffusion, we use a complex network evolutionary game method to explore the dynamic impacts of government policies on EV diffusion in different scale networks. The results show that with the increase in manufacturers in the network, the degree of EV diffusion increases accordingly at the same government policy level. In the present network scale of manufacturers, on the supply side, the effects of tax and subsidy policies can respectively promote EV diffusion to 0.82 and 0.86. Fuel vehicle (FV) license plate restrictions and consumer purchase subsidies are effective policies on the demand side, which both can promote EV diffusion to 0.84. Compared with consumer purchase subsidies, production subsidies for manufacturers have better effects on EV diffusion. Neither the tax and subsidy policies nor the FV license plate restriction and EV purchase subsidy policies can realize full EV diffusion. Finally, combined with the current status of China's automobile industry, the corresponding policy recommendations for different periods are given.
The Indian seafood sector is at the center of the country's economic system, contributing largely to export earnings and rural incomes. However, the COVID-19 pandemic has exposed critical vulnerabilities to its supply chain, including logistical limitations, labor shortages, and disruptions to global trade. This study investigates resilient and sustainable supply chain management practices for strengthening global competitiveness after the pandemic. A mixed-methods research design was used, where primary data were collected from different stakeholders in the sector, that is, exporters, processors, and fishermen, while secondary data were collected from industry and government reports. Arguably, the study found that digitalization, enhancement of cold chain logistics, sustainable fishing, and export market diversification are critical in enhancing resilience. Further, stakeholder collaboration, investment in processing plants fueled by renewable energy, and adherence to international standards of sustainability have become key strategies. The study identifies that long-term competitiveness relies on the integration of sustainability with resilience, thereby ensuring operational competitiveness while meeting environmental and social responsibilities. The study provides significant contributions to policy formulation and strategic planning, providing a blueprint for the success of the Indian seafood sector in an increasingly dynamic global market.
The advent of novel power systems, predominantly reliant on renewable energy sources such as wind and photovoltaics, has precipitated a surge in demand for energy storage solutions. Buildings are undergoing a metamorphosis, emerging as pivotal actors in the realm of electricity generation and consumption, with vast untapped potential for energy storage. However, current research is marred by a dearth of quantitative methodologies for assessing the existing virtual energy storage (VES) resources within building contexts. As a result, it is challenging to provide an accurate evaluation of their potential value and components. In this study, an equivalent battery model is employed, comprising parameters such as equivalent charging and discharging power and energy storage capacity. Integration of VES into traditional energy storage(TES) frameworks. The potential and composition of VES resources within the building area is analyzed. Then, the VES potential of vehicle-to-home system and heat pumps and building thermal capacity are analyzed for winter electric heating in Beijing. The results show that VES system is capable of delivering a maximum equivalent charging power of 432.816 kW, a maximum equivalent discharging power of 385.376 kW, and an equivalent energy storage capacity of 2165.64 kWh. VES can effectively participate in energy management in rural electric heating through rational design and scheduling. No configuration of TES is required. The objective of this work energy planning in the building sector is to provide practical quantitative tools and strategies. It provides guidance on the design and optimization of future distributed energy systems.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Jonathan C. Denman, Xinyu Zhao, Jennifer Colborn
et al.
Large eddy simulations (LES) are conducted in this study to understand the convective and radiative heat transfer characteristics within a backward-facing step combustor. The Penn State backward-facing step combustor is modeled and the experimental signals are directly compared with computational results to validate physical models and numerical procedures. The baseline simulation features a wall-resolved LES of the full-combustor geometry for a lean methane/air mixture at an equivalence ratio of 0.55. A 16-species skeletal mechanism is employed with a dynamic thickened flame model to capture turbulence-chemistry interactions. A dynamic Smagorinsky model is employed to capture the subgrid-scale stress. A Monte-Carlo ray tracing based radiation solver is employed with a highly accurate line-by-line spectral database to post-process LES solutions to obtain the radiative heat fluxes. Comparison between the baseline results after accounting for experimental facility constraints show excellent agreement in radiative heat fluxes at four sensor locations. The total heat fluxes consisting of both radiation and convection is under-predicted by approximately 30%. Further parametric studies that use different spanwise dimensions, chemical kinetic models, molecular transport models, and thickening factors show that the better prediction of the temperature and flame speed of GRI-mech 3.0 can increase the prediction of convective heat transfer, while maintaining a similar comparison in the prediction of radiative heat transfer. The molecular transport model is also critical for the well-resolved LES to correctly capture the flame brush angles. The turbulence-chemistry interaction effects seem to be well-captured by the grid and have a negligible impact on the results. Compared to the reduced-span geometry that is frequently employed in backward-facing step configuration simulations, the full-span geometry is shown to be significant for capturing flame stabilization and heat transfer characteristics. Finally, limitation of this model validation study is discussed.
Fuel, Energy industries. Energy policy. Fuel trade
Abstract Green hydrogen production via photovoltaic (PV)‐electrolysis is a promising method for addressing global climate change. The battery provides a stable power supply for the PV‐electrolysis system. Hence, this study proposes a robust model for configuring the capacity of a PV‐battery‐electrolysis hybrid system by considering the dynamic efficiency characteristics and cost learning curve effect of key equipments. As a segmented function, the dynamic efficiency of electrolysis is incorporated into the robust model, which describes the hydrogen production efficiency based on power fluctuations. A learning curve model is developed based on historical data from 2012 to 2020 to predict future capital expenditure. Major results are as follows: (1) The use of dynamic efficiency characteristics can reflect the real‐time status of the electrolysis more accurately, and make the capacity configuration more reasonable compared with fixed efficiency. (2) Considering the effect of the learning curve, by 2050, the capital expenditure of the PV panel and proton exchange membrane electrolysis can be dropped to 2981 and 1992 CNY/kW, respectively. (3) The optimal case considering uncertainty currently is a 1 MW PV panel equipped with 242 kW electrolysis and 2276 kW battery.
Energy industries. Energy policy. Fuel trade, Production of electric energy or power. Powerplants. Central stations
Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterward, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.
Energy industries. Energy policy. Fuel trade, Production of electric energy or power. Powerplants. Central stations
This paper presents PyPSA-GB, a dataset and model of Great Britain’s (GB) power system encompassing historical years and the future energy scenarios developed by National Grid. It is the first fully open-source model implementation of the future GB power system with high spatial and temporal resolution, and data for future years up to 2050. Two power dispatch formulations can be optimised: (i) single bus unit commitment problem, and (ii) network constrained linear optimal power flow. The model is showcased through an example analysis of quantifying future wind curtailment in Scotland. PyPSA-GB provides an open-source basis for GB operational and planning studies, e.g., sector coupling and flexibility options.
Materials informatics has emerged as a powerful tool for the discovery, design, and optimization of materials with tailored thermal radiative properties. This perspective review highlights the recent advances in optimization algorithms, including Bayesian optimization, deep learning, and quantum computing, and their applications in various fields such as thermophotovoltaics, radiative cooling, gas sensors, and directional emitters. We also discuss the challenges and future directions of this rapidly evolving field.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Abstract Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models’ performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach that integrates metaheuristic optimisation with an ensemble of five widely used neural network architectures for time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.
We study the energy dependence of the total and diffractive neutrino-nucleon and neutrino-nucleus cross sections at very high energies. The calculation employs the QCD dipole model and the small-$x$ nonlinear Balitsky-Kovchegov evolution. We show the sensitivity of the nuclear effect quantification on the nuclear setup, and predict up to $\sim 10\%$ nuclear suppression in the inclusive neutrino-oxygen scattering stemming from the nonlinear evolution. Diffractive contribution to the total scattering is small, which is only few percentage. The $\left|q\bar{q}g\right>$ componnent of the $W^{\pm}$ boson is found to contribute significantly to the diffractive process, which reaches up to $\sim 40\%$ of the diffractive cross section.
While wind and solar power contribute to sustainability, their intermittent nature poses challenges when integrated into the grid. To mitigate these issues, renewable energy can be combined with coal fired power and hydropower sources to stabilize the energy system, with battery storage serving as a backup source to smooth the total output. This study develops a low carbon dispatch model for a combined energy system using a two stage stochastic optimization approach. The model incorporates a carbon trading mechanism to regulate emissions and addresses the uncertainty in wind and solar outputs by clustering output curves into typical scenarios to derive a joint distribution. In the initial stage of scheduling, decisions are made regarding the unit commitment for coal fired power plants. The second stage optimizes the expected operation cost of other energy generation sources. The feasibility of the model is demonstrated by comparing the results of stochastic and deterministic scenarios through simulation. Analysis of different carbon prices further explores the impact of the carbon trading mechanism on the system's operation cost.
Abstract Replacing fossil fuels with renewable energy sources is considered as an effective means to reduce carbon emissions at the industrial level and it is often supported by local authorities. However, individual firms still encounter technical and financial barriers that hinder the installation of renewables. The eco-industrial park approach aims to create synergies among firms thereby enabling them to share and efficiently use natural and economic resources. It also provides a suitable model to encourage the use of renewable energy sources in the industry sector. Synergies among eco-industrial parks and the adjacent urban areas can lead to the development of optimized energy production plants, so that the excess energy is available to cover some of the energy demands of nearby towns. This study thus provides an overview of the scientific literature on energy synergies within eco-industrial parks, which facilitate the uptake of renewable energy sources at the industrial level, potentially creating urban-industrial energy symbiosis. The literature analysis was conducted by arranging the energy-related content into thematic categories, aimed at exploring energy symbiosis options within eco-industrial parks. It focuses on the urban-industrial energy symbiosis solutions, in terms of design and optimization models, technologies used and organizational strategies. The study highlights four main pathways to implement energy synergies, and demonstrates viable solutions to improve renewable energy sources uptake at the industrial level. A number of research gaps are also identified, revealing that the energy symbiosis networks between industrial and urban areas integrating renewable energy systems, are under-investigated.
This research paper examines the impact of the EU Emissions Trading System (EU ETS) on the development of the renewable energy market in the European Union. Using regression analysis, the study investigates the relationship between the volume of emission permits, EU GDP, and the share of renewables in the overall energy balance. The results reveal that the EU ETS has a significant negative impact on the development of the renewable energy market in the EU, with an increase in the volume of emission permits corresponding to a decrease in the share of renewables. The study also finds that economic growth alone may not necessarily lead to an increase in the share of renewables; however, when combined with other policies and measures, economic growth may promote the development of renewable energy in the EU. The findings suggest that the EU ETS needs to be improved to encourage renewable energy development effectively, and policymakers should consider introducing additional policies and measures to promote the deployment of renewable energy. In addition to analysing the impact of the EU ETS on the renewable energy market, this paper provides an overview of the EU ETS and its challenges. As a cap-and-trade system covering over 11,000 installations across the European Economic Area, the EU ETS has faced challenges from the beginning due to an oversupply of emission allowances. This resulted in a low carbon price insufficient for driving climate change mitigation. To address these issues, the EU ETS Market Stability Reserve (MSR) was established to absorb excess allowances from the market and manage past surpluses. However, the MSR cannot handle sudden shocks or future surpluses. The paper also presents data on the distribution of emission allowances by country and sector. Germany has the most significant emissions under the EU ETS, followed by Italy, Poland, and the United Kingdom. The stationary installations sector, encompassing power plants and other large industrial emitters, received the most allowances, followed by the fuel combustion sector. The data indicates that energy and industry are the largest emitters and thus receive the most ETS allowances. This research contributes to the understanding of the EU ETS and its impact on the development of the renewable energy market in the EU. The results emphasise the need to improve the EU ETS to promote the deployment of renewable energy and suggest that policymakers should consider introducing additional policies and measures to facilitate the transition to a low-carbon economy.