Hasil untuk "Energy industries. Energy policy. Fuel trade"

Wen Huwei, C. Shuai, Lee Chien-Chiang

Faced with the global climate change, as a major greenhouse gas emitter, China launched a pilot policy on low-carbon city construction since 2010. Few studies have discussed how climate policies affect the investment and financing behavior of energy-intensive enterprises. Based on the micro data of A-share listed enterprises in China’s energy-intensive industries, this study aims to assess the productivity effect of low-carbon city pilot (LCCP) policy and investigates the mechanism of financing and investment using the difference-in-difference method. Empirical results provide evidence that the LCCP policy has significantly improved the total factor productivity of energy-intensive enterprises. In terms of the mechanisms, the LCCP policy has increased the supply of bank credit to enterprises and encouraged their long-term investment in fixed assets and R&D activities. The productivity effect of the LCCP policy is greater for state-owned enterprises and enterprises with political connection. Urban human capital, industrial agglomeration, and resource endowment contribute to the productivity effect of LCCP policy for enterprises in the energy-intensive industries. The findings show that the LCCP is an effective comprehensive policy to promote the high-quality development of energy-intensive industries, and the findings also provide enlightenment for enacting better climate transition policies.

K.J. Rajimon, Rajiv Gandhi Gopalsamy

Perovskite, oxide, organic, and dye-sensitised solar cells are studied from 2015 to 2025, and their current standing and future Mott-Schottky (MS) analysis in photovoltaic (PV) research are highlighted in this review. The incorporation of MS characterisation methodology with solar cell capacitance simulator one dimension (SCAPS-1D) simulations, ab-initio calculations, impedance spectroscopy, and nascent data-driven models is addressed. The MS approach will always be at the forefront in the extraction of the flat band potential, doping concentration, depletion region width, and built-in potential. This is the link between the energetics of the semiconductors and the charge transport of the solar cells and other PV. With MS-validated doping profile optimisation, interface engineering achieves (37.66%) power conversion efficiencies, 1.52 V (open-circuit voltages) and fill factors above (87%). Unfortunately, there are limitations of the frequency-dependent capacitance, parasitic elements, trap states, and non-ideal depletion layer of some architectures, like organic and hybrid ones. The MS and simulations to be used together, and machine learning adoption and analytical models to improve the electronic characterisation, have the potential to resolve the problems. This study offers a critical evaluation of current methods and inherent constraints in MS analysis, offering a strategic framework for the systematic design of efficient, durable, and sustainable solar technologies.

I. Purnama, Anisa Mutamima, M. Aziz et al.

Palm oil is an efficient feedstock for biodiesel production due to its high oil yield and cost‐effectiveness, positioning it as a key component in the global biofuel industry. However, the expansion of oil palm plantations has raised substantial environmental and socio‐economic concerns. This review critically assesses the environmental impacts of palm oil biodiesel, including greenhouse gas emissions, deforestation, biodiversity loss, and the degradation of water and soil resources. Additionally, it explores the “food versus fuel” debate, emphasizing how competition for land and resources between biodiesel production and food cultivation affects global food security, particularly in developing nations. What distinguishes this review is its focus on Southeast Asian producer countries, particularly Indonesia and Malaysia, where biodiesel policies and land‐use changes intersect with food and environmental systems in unique ways. Unlike previous studies, this article delves into the often‐overlooked consequences of peatland conversion, highlighting its role in exacerbating carbon emissions and biodiversity loss. By providing a detailed analysis of the socio‐economic trade‐offs and sustainability challenges linked to palm oil biodiesel, the review offers insights into the complex interplay between renewable energy, food security, and environmental stewardship. It also evaluates technological innovations and best practices that can mitigate negative impacts. Furthermore, the review critically examines certification initiatives like the roundtable on sustainable palm oil (RSPO) and the indonesian sustainable palm oil (ISPO) and their effectiveness in promoting sustainable practices. By integrating case studies, this article demonstrates practical applications of these principles, offering actionable recommendations for policymakers, industry stakeholders, and researchers in the field.

Lihong Li, Kun Song, Weimao Xu et al.

With the rapid growth of global energy demand, the fossil fuel-dominated electric power industry has led to serious environmental problems. Tradable green certificates (TGC) and carbon emission trading (CET) have become key mechanisms for promoting sustainable development of the electricity market by serving as market-oriented policy tools. To deeply analyze the impact of TGC and CET on the sustainable development of China’s electricity market and provide a scientific basis for policymakers. This study uses system dynamics (SD) methods to construct a policy synergy analysis framework for TGC and CET. It explores the impact mechanism of dual policy incentives on the sustainable development of the electricity market. Firstly, the current application status of TGC and CET in China was reviewed. Based on the literature analysis, identify key factors that affect the sustainable development of the electricity market. Then, by deconstructing the interaction between TGC policy and CET policy, an SD model was established that includes multidimensional feedback such as policy, technology, funding, and market, and the dynamic functional relationships in the SD model were quantified. Finally, Vensim PLE software 7.3.2 was used to simulate the evolution of sustainable development in the electricity market under different policy scenarios. The research results indicate that (1) the adjustment of the TGC quota ratio can change the supply and demand mechanism to form a price leverage effect, effectively stimulate the growth of renewable energy generation capacity, and accelerate the low-carbon transformation of power enterprises; and (2) the CET market changes the cost structure of power generation through carbon price signals. When the carbon emission cap target tightens, CET prices quickly rise, leading to a significant trend of carbon reduction in the electricity market; (3) the application of policy combinations can significantly promote the sustainable development of the electricity market, but the unreasonable setting of policy parameters can trigger market risks. Therefore, policy design should focus on flexibility and implement appropriate policy combinations at different stages of electricity market development to promote green transformation while ensuring smooth market operation. This study innovatively reveals the synergistic effect of TGC and CET in the sustainable development of the electricity market from a systems theory perspective. The research results provide a scientific basis for decision-makers to formulate policy adjustment plans and have essential reference value for achieving the dual goals of energy structure transformation and electricity market stability.

A. Safari, Nandini Das, O. Langhelle et al.

Current discourse on the transition to a decarbonized energy system future is dominated by renewable energy solutions. Initial conditions for this transition may vary across different regions and countries. There are, however, also opportunities for innovative solutions that utilize other low‐carbon energy sources and technology mix. Sustainable development is a contested concept and varies with priorities attached to social, economic, and environmental goals. Therefore, the one‐size‐fits‐all type of solution paradigm needs to be broadened, to accelerate action in the short to medium term. Our argument is that natural gas can be an important complementary transition fuel to support renewable energy in the short‐ and medium‐term transition phases. This means that the goal of zero fossil fuel as a short‐ and medium‐term solution needs to be reconsidered. This takes us to the next argument that innovation and upgraded technology in the low‐carbon fossil fuel sector will provide an important impetus for low‐carbon transition, which we see as a phase lasting until the middle of the century. However, the transition toward a sustainable energy future of gas‐fueled solutions has challenges from the social, technical, economic, geographical, and political points of view. Suitable local solutions should, however, also be assessed. These should take into consideration infrastructure, local demands, resources, and economic aspects as well as national energy policies. An analysis based on the experiences of four countries, both developed and developing, is presented in this study. The countries selected for this study can be placed in two categories: those with an abundance of natural gas reserves (Iran and Norway) and those that are import‐dependent (India and UK). The cross‐country analysis will help us to understand the realistic challenges and opportunities of natural gas as a transition fuel.

E. Tianlong, Kai Qin, Zhao Liu et al.

Abstract Ultra-high-voltage (UHV) autotransformers are widely employed in long-distance power transmission systems. Their operation involves complex energy conversion and coupling mechanisms, including high-intensity magnetic induction energy and strong induced currents. From the perspective of power systems and automation control, it is essential to construct a comprehensive equivalent control circuit for UHV autotransformers, integrating the analysis of induced current and magnetic flux density into the domain of analog electronics. Numerical analysis has become a core approach for investigating the external thermal physical characteristics of transformer power and various thermal management strategies. In this paper, the Message Passing Interface (MPI) and Portable, Extensible Toolkit for Scientific Computation (PETSc) parallel computing framework is adopted to compute and analyze the electro-thermal coupling in a UHV autotransformer. The dielectric loss of transformer components is thoroughly examined. A linear numerical simulation method for evaluating dielectric loss is assessed through parallel computation and validated via the design of a three-dimensional coupling model for leakage flux and core temperature rise. The dielectric loss calculation is applied to the transformer. Magnetostriction measurements under rated output power and various current and voltage conditions reveal the correlation between the coupled data and the thermal topology. The MPI-PETSc framework significantly enhances the computational efficiency of three-dimensional electro-thermal coupling problems in UHV autotransformers through distributed computing and efficient numerical solving, making it suitable for large-scale, high-precision engineering simulations.

Jing Zhang, Pei Li, Junfeng Wan et al.

Proper management of nitrogen-containing pig manure is crucial to realize its benefits of supporting plants-grow as fertilizer while minimizing its impact on the environment and climate change. Dry collection, rinsing and water submerging are manure cleaning techniques adopted in different types of pig farms and in different regions. As the first step of manure management, manure cleaning technique affects manure generation and nitrogen flow in the subsequent treatment and utilization processes. This short communication is to discuss different manure cleaning techniques and their impacts on nitrogen flow through pig manure management processes. Reducing nitrogen losses should focus on solid manure treatment such as composting when manure is dry collected. More diversified pathways of nitrogen losses are possible when manure is cleaned using water submerging technique. It is thus needed to develop proper and specific nitrogen management strategies and technologies, taking into account the manure cleaning technique adopted in pig farms.

Sydney Erickson, Martin Millon, Padmavathi Venkatraman et al.

Strongly lensed Active Galactic Nuclei (AGN) with an observable time delay can be used to constrain the expansion history of the Universe through time-delay cosmography (TDC). As the sample of time-delay lenses grows to statistical size, with $\mathcal{O}$(1000) lensed AGN forecast to be observed by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), there is an emerging opportunity to use TDC as an independent probe of dark energy. To take advantage of this statistical sample, we implement a scalable hierarchical inference tool which computes the cosmological likelihood for hundreds of strong lenses simultaneously. With this new technique, we investigate the cosmological constraining power from a simulation of the full LSST sample. We start from individual lenses, and emulate the full joint hierarchical TDC analysis, including image-based modeling, time-delay measurement, velocity dispersion measurement, and external convergence prediction. We fully account for the mass-sheet and mass-anisotropy degeneracies. We assume a sample of 800 lenses, with varying levels of follow-up fidelity based on existing campaigns. With our baseline assumptions, within a flexible $w_0w_a$CDM cosmology, we simultaneously forecast a $\sim$2.5% constraint on H0 and a dark energy figure of merit (DE FOM) of 6.7. We show that by expanding the sample from 50 lenses to include an additional 750 lenses with plausible LSST time-delay measurements, we improve the forecasted DE FOM by nearly a factor of 3, demonstrating the value of incorporating this portion of the sample. We also investigate different follow-up campaign strategies, and find significant improvements in the DE FOM with additional stellar kinematics measurements and higher-precision time-delay measurements. We also demonstrate how the redshift configuration of time-delay lenses impacts constraining power in $w_0w_a$CDM.

Lane D. Smith, Daniel S. Kirschen

Net energy metering has been a successful policy for increasing solar generation installations and reducing the costs of photovoltaic arrays for consumers. However, increased maturity of solar technologies and concerns over cost shifts created by net energy metering have recently caused the policy to change its incentives. What once favored behind-the-meter solar generation now is focused on compensating flexible operation. This paper explores the impacts that different net energy metering policies have on commercial consumers with various distributed energy resources. We show that the newest iteration of net energy metering is less beneficial for consumers with only solar generation and instead favors those that pair energy storage with solar. Though shiftable flexible demand offers consumers the ability to operate flexibly, the export prices offered by the latest net energy metering policy provide limited value to flexible demand.

Emidio Gabrielli

The exploration of fundamental quantum phenomena, such as entanglement and Bell inequality violations$-$extensively studied in low-energy regimes$-$has recently extended to high-energy particle collisions. Experimentally, Bell inequality violations, which challenge Einstein's principle of local realism, were first observed in low-energy entangled photon systems by A. Aspect, J. F. Clauser, and A. Zeilinger, earning them the 2022 Nobel Prize in Physics. Particle colliders provide a novel setting for probing quantum information theory, operating at energies over ten orders of magnitude higher than previous experiments and in the presence of electroweak and strong interactions. Additionally, collider detectors offer unique advantages for quantum state reconstruction via quantum state tomography. This book chapter reviews key theoretical and experimental advancements in this emerging field, highlighting its challenges, objectives, and potential impact on both quantum information theory and high-energy physics.

Junqi Guo

This study comprehensively reviews multiple research regarding the impact of fossil fuel price fluctuations on carbon emission trading prices. Presently, coal is the largest energy source in China and the largest contributor to carbon emissions. However, as the green economy develops and the global landscape changes, requirements for carbon emissions grow stricter. The manufacturing industry in China is booming while enterprises related to the carbon market are facing immense pressure to reduce emissions under the mechanism of carbon pricing and trading. Fossil fuel use is the largest source of carbon emissions in China. Therefore, carbon trading mechanisms and carbon tax policies are key cost factors for the enterprises involved. This study aims to discuss the impact of fossil fuel price fluctuations on carbon emission trading prices in China, draw relevant conclusions, and propose corresponding suggestions. From the government's perspective, they can establish a dynamic adjustment mechanism for carbon emission trading prices. Meanwhile, enterprises should establish a mechanism to forecast carbon emission trading prices, enabling them to comprehensively manage the impact of price fluctuations driven by changes in fossil fuel prices.

Yuling Pan, Feng Dong

John Akumu Okoth, Peter Musau Moses, Mathews Amuti

The global shift to net-zero greenhouse gas (GHG) emissions and a decarbonized energy sector to mitigate climate change presents far reaching opportunities and challenges to the maritime sector. The port acts as a major facilitator to global trade bringing convergence to various forms of transport to interlink trade hubs and hence presents a good opportunity for ports to develop facilities for bulk renewable energy solutions. The ports have the capacity to utilize their existing land, infrastructure, resources and grid connectivity for power generation, storage, and supply. The International Maritime Organization (IMO) has developed policies and regulations towards eliminating conventional fuels from the maritime sector and encourages its member states on use of alternative fuels and the development of green energy hubs within ports. There is therefore an ever-increasing demand for renewable energy sources to meet the foreseeable huge demands that will emanate from shipping, transport, and industries. However, developing the energy production capability requires adequate planning, capacity building, investments, and partnerships. The Lamu Port in Kenya presents a unique opportunity towards developing a green energy hub to meet this demand for alternative fuels. This study will outline this possibility while considering availability, demand, policy, ownership, and a brief financial outlook.

X. Liu, G. Wang, J. Si et al.

The open jet flame in hot co-flow (JHC) has been frequently utilized for fundamental investigations of Moderate or Intense Low-oxygen Dilution (MILD) combustion due to its controllable conditions and relatively easy measurement capabilities. However, practical MILD combustion must take place within a combustor that is enclosed. Therefore, it is necessary to examine the similarity and disparity of combustion characteristics between two flame configurations. This issue is addressed currently. Specifically, we investigate the flow mixing, ignition, and combustion, as well as emission characteristics of non-premixed and premixed JHC and cylindrical furnace (FUR) flames at various values of the environmental temperature (Te) and central jet Reynolds number (Re). For the open non-premixed flames, we employ both previous single-tube JHC (SJHC) combustor and presently modified JHC (MJHC) one that uses the same nozzle configuration as the FUR burner. It is revealed that significant differences occur in flow, combustion and emission characteristics between the SJHC and FUR cases. On the other hand, both non-premixed and premixed MJHC configurations exhibit high similarity to the corresponding FUR cases in terms of upstream flow mixing and combustion features. Moreover, different CO and NOx emissions result from open JHC and close furnace flames due to different post-combustion configuration and residence time. Accordingly, future experiments on non-premixed MJHC and premixed JHC flames are highly recommended for better understanding practical MILD combustion.

Seyed Soroush Karimi Madahi, Gargya Gokhale, Marie-Sophie Verwee et al.

A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. Model-free reinforcement learning (RL) methods are an appropriate choice for solving the energy arbitrage problem due to their outstanding performance in solving complex stochastic sequential problems. However, RL is rarely deployed in real-world applications since its learned policy does not necessarily guarantee safety during the execution phase. In this paper, we propose a new RL-based control framework for batteries to obtain a safe energy arbitrage strategy in the imbalance settlement mechanism. In our proposed control framework, the agent initially aims to optimize the arbitrage revenue. Subsequently, in the post-processing step, we correct (constrain) the learned policy following a knowledge distillation process based on properties that follow human intuition. Our post-processing step is a generic method and is not restricted to the energy arbitrage domain. We use the Belgian imbalance price of 2023 to evaluate the performance of our proposed framework. Furthermore, we deploy our proposed control framework on a real battery to show its capability in the real world.

U. Damo, M. L. Ferrari, A. Turan et al.

Abstract This paper reports a review of an environmentally clean and efficient source of energy such as solid oxide fuel cell hybrid systems. Due to climate concerns, most nations are seeking alternative means of generating energy from a clean, efficient and environmental-friendly method. However, this has proven a big hurdle for both academic and industry researchers over many years. Currently, practical and technically feasible solution can be obtained via an integration of a microturbine and a fuel cell (hybrid systems). Combining the two distinct systems in a hybrid arrangement the efficiency of the microturbine increases from 25 to 30% to the 60–65% range. Hence, this paper outlines an engineering power generation solution towards the acute global population growth, the growing need, environmental concerns, intelligent use of energy with attendant environmental and hybrid system layouts concerning arising problems and tentative proposed solutions. Furthermore, advantages of a solid oxide fuel cell hybrid systems with respect to the other technologies are identified and discussed rationally. Special attention is devoted to modelling with software and emulator rigs and system prototypes. The paper also reviews the limitations and the benefits of these hybrid systems in relationship with energy, environment and sustainable development. Few potential applications, as long-term potential actions for sustainable development, and the future of such devices are further discussed.

Clara McDonnell, J. Gupta

ABSTRACT Institutional investors, who control as much as $154 trillion globally, may play an important role in shaping the energy transition as major stakeholders in fossil fuel producing, distributing and consuming companies. Research on investors and fossil fuels has focused largely on the divestment movement or on shareholder engagement. However, given their limited success to date, additional strategies to influence the fossil fuel sector are merited. This review paper expands the scope of attention to investors, asking: what strategies for influencing the fossil fuel industry are available to institutional investors and what are the implications of these for achieving an inclusive fossil fuel phase-out? Through a systematic review of 153 papers, we identify seven strategies for influencing the fossil fuel phase-out: divestment, shareholder engagement, hiring practices, engaging the financial sector, engaging indirect financial actors, litigation, and green investment. These strategies represent ways for investors to increase the impact of their engagements, as well as areas deserving greater attention from academics, policymakers, and activists. Across these strategies, we note trade-offs in favour of financial returns at the expense of social, ecological, and equity outcomes. We argue that future research should focus on: (a) the role of under-studied actors in aligning finance with climate goals; (b) the implications of investor action for an inclusive energy transition; and (c) policy solutions capable of overcoming investors’ short-term profit motives to instead incentivise long-term investor engagement with climate issues. Key policy highlights Legal mandates and uncertainties in how to apply fiduciary responsibility with respect to climate change result in investors and asset managers prioritizing short-term profits at the expense of climate goals. Voluntary investor efforts focus predominantly on transparency and disclosure and are insufficient to meet climate goals. Legally binding decarbonization strategies are needed to align finance with the Paris Agreement. Influence over investment strategies is increasingly concentrated in a small number of powerful actors. Policymakers should consider not only asset owners, but also asset managers, index providers, and proxy advisory firms. Investors can increase their climate action by incorporating policies on climate into their mandates for asset managers and engaging with fossil fuel financiers.

D. Weisbach, Samuel Kortum, Michael B. Wang et al.

Climate policies vary widely across countries, with some countries imposing stringent emissions policies and others doing very little. When climate policies vary across countries, energy-intensive industries have an incentive to relocate to places with few or no emissions restrictions, an effect known as leakage. Relocated industries would continue to pollute but would be operating in a less desirable location. We consider solutions to the leakage problem in a simple setting where one region of the world imposes a climate policy and the rest of the world is passive. We solve the model analytically and also calibrate and simulate the model. Our model and analysis imply: (1) optimal climate policies tax both the supply of fossil fuels and the demand for fossil fuels; (2) on the demand side, absent administrative costs, optimal policies would tax both the use of fossil fuels in domestic production and the domestic consumption of goods created with fossil fuels, but with the tax rate on production lower due to leakage; (3) taxing only production (on the demand side), however, would be substantially simpler and almost as effective as taxing both production and consumption, because it would avoid the need for border adjustments on imports of goods; and (4) the effectiveness of the latter strategy depends on a low foreign elasticity of energy supply, which means that forming a taxing coalition to ensure a low foreign elasticity of energy supply can act as a substitute for border adjustments on goods.

Debojyoti Chakraborty, Jayeeta Mondal, Hrishav Bakul Barua et al.

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.