Search Results (427)

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

Marine–continental transitional shale is a promising unconventional gas reservoir, playing an increasingly important role in China’s energy portfolio. However, compared to marine shale, research on marine–continental transitional shale’s fractal characteristics of pore structure and complete pore size distribution remains limited. In this work, high-pressure mercury intrusion, N₂ adsorption, and CO₂ adsorption techniques, combined with fractal geometry modeling, were employed to characterize the pore structure of the Shanxi Formation marine–continental transitional shale. The shale exhibits generally high TOC content and abundant clay minerals, indicating strong hydrocarbon-generation potential. The pore size distribution is multi-modal: micropores and mesopores dominate, contributing the majority of the specific surface area and pore volume, whereas macropores display a single-peak distribution. Fractal analysis reveals that micropores have high fractal dimensions and structural regularity, mesopores exhibit dual-fractal characteristics, and macropores show large variations in fractal dimension. Characteristics of pore structure is primarily controlled by TOC content and mineral composition. These findings provide a quantitative basis for evaluating shale reservoir quality, understanding gas storage mechanisms, and optimizing strategies for sustainable of oil and gas development in marine–continental transitional shales. Full article

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along with relevant ambient weather data and building characteristics, with the objective of predicting the buildings’ energy performance through the year 2040. Using the forecasted emission results, the portfolio of buildings is analyzed for the incurred carbon non-compliance fees based on their on-site fossil fuel CO_2e emissions to assess and pinpoint facilities with poor energy performance that need to be prioritized for decarbonization. The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. To comply with these regulations, the building portfolio would need to reduce on-site fossil fuel CO_2e emissions by an average of 58,246 metric tons (22.10 kg/sq. ft.) annually, totaling 640,708 metric tons (22.10 kg/sq. ft.) over a period of 11 years. This study demonstrates the potential for robust machine learning models to generate accurate forecasts to evaluate carbon compliance and guide prompt action in decarbonizing the built environment. Full article

Understanding the price dynamics of oil futures is crucial for advancing green finance strategies and supporting sustainable energy transitions. This study investigates the macroeconomic and energy market determinants of oil futures prices through Granger causality, cointegration analysis, and the error correction model, using daily data. It focuses on the influence of economic development levels, exchange rate fluctuations, and inter-energy price linkages. The empirical findings indicate that (1) oil futures prices exhibit strong correlations with other energy prices, macroeconomic factors, and exchange rate variables; (2) economic development significantly affects oil futures prices, while exchange rate impacts are statistically insignificant based on the daily data analyzed; (3) there exists a stable long-term equilibrium relationship between oil futures prices and variables representing economic activity, exchange rates, and energy market trends; (4) oil futures prices exhibit significant short-term dynamics while adjusting steadily toward a long-run equilibrium driven by macroeconomic and energy market fundamentals. By enhancing the accuracy of oil futures price forecasting, this study offers practical insights for managing financial risks associated with fossil energy markets and contributes to the formulation of low-carbon investment strategies. The findings provide a valuable reference for integrating energy pricing models into sustainable finance and climate-aligned portfolio decisions. Full article

Direct Air Capture (DAC) is emerging as a critical climate change mitigation strategy, offering a pathway to actively remove atmospheric CO₂. This comprehensive review synthesizes advancements in DAC technologies, with a particular emphasis on the pivotal role of nanostructured solid sorbent materials. The work critically evaluates the characteristics, performance, and limitations of key nanomaterial classes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, amine-functionalized polymers, porous carbons, and layered double hydroxides (LDHs), alongside solid-supported ionic liquids, highlighting their varied CO₂ uptake capacities, regeneration energy requirements, and crucial water sensitivities. Beyond traditional temperature/pressure swing adsorption, the review delves into innovative DAC methodologies such as Moisture Swing Adsorption (MSA), Electro Swing Adsorption (ESA), Passive DAC, and CO₂-Binding Organic Liquids (CO₂ BOLs), detailing their unique mechanisms and potential for reduced energy footprints. Despite significant progress, the widespread deployment of DAC faces formidable challenges, notably high capital and operational costs (currently USD 300–USD 1000/tCO₂), substantial energy demands (1500–2400 kWh/tCO₂), water interference, scalability hurdles, and sorbent degradation. Furthermore, this review comprehensively examines the burgeoning global DAC market, its diverse applications, and the critical socio-economic barriers to adoption, particularly in developing countries. A comparative analysis of DAC within the broader carbon removal landscape (e.g., CCS, BECCS, afforestation) is also provided, alongside an address to the essential, often overlooked, environmental considerations for the sustainable production, regeneration, and disposal of spent nanomaterials, including insights from Life Cycle Assessments. The nuanced techno-economic landscape has been thoroughly summarized, highlighting that commercial viability is a multi-faceted challenge involving material performance, synthesis cost, regeneration energy, scalability, and long-term stability. It has been reiterated that no single ‘best’ material exists, but rather a portfolio of technologies will be necessary, with the ultimate success dependent on system-level integration and the availability of low-carbon energy. The review paper contributes to a holistic understanding of cutting-edge DAC technologies, bridging material science innovations with real-world implementation challenges and opportunities, thereby identifying critical knowledge gaps and pathways toward a net-zero carbon future. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

The Non-Wire Alternative (NWA) approach has gained attention as a strategy to replace or defer traditional grid infrastructure upgrades by leveraging integrated solutions combining Energy Storage Systems (ESSs) with Distributed Energy Resources (DERs). The overall feasibility and economics of distributed flexibility solutions can be enhanced by leveraging the synergies among various DERs for NWA deployment. This study presents the results of a techno-economic analysis of an NWA portfolio that integrates Photovoltaic (PV) generation and Demand Response (DR) resources with ESSs. Three representative load profiles are analyzed under different load growth scenarios: a balanced mix of industrial, commercial, and residential loads; residential-dominant loads; and commercial/industrial-dominant loads. The analysis shows that the combined deployment of PVs and DRs significantly reduces the required ESS capacity. Furthermore, economic analysis based on Benefit–Cost Analysis (BCA) demonstrated that combining ESSs with either PVs or DRs enhances economic efficiency compared with an NWA portfolio that relies on ESSs alone, particularly under low-capacity factor conditions. However, the effectiveness of a DR or PV varies depending on the load profile. DR is less effective when the peak load durations are prolonged, whereas PV offers limited economic benefits under residential loads with the evening peak demand. These techno-economic results highlight the importance of tailoring NWA portfolios to specific load conditions to maximize both technical performance and economic value. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article

Accelerating the low-carbon transition of China’s coal-fired power sector is essential for advancing national sustainability goals and fulfilling global climate commitments. This study introduces an integrated, data-driven analytical framework to facilitate the sustainable transformation of the coal power sector through coordinated unit-level retirements, new capacity planning, and targeted retrofits. By combining a comprehensive unit-level database with a multi-criteria evaluation framework, the analysis incorporates environmental, technical, and economic factors into decision-making for retirement scheduling. Scenario analyses based on the China Energy Transformation Outlook (CETO 2024) delineate both baseline and ideal carbon neutrality pathways. Optimization algorithms are employed to identify cost-effective retrofit strategies or portfolios, minimizing levelized carbon reduction costs. The findings reveal that cumulative emissions can be reduced by 10–14.9 GtCO₂ by 2060, with advanced technologies like CCUS and co-firing contributing over half of retrofit-driven mitigation. The estimated transition cost of 6.2–6.7 trillion CNY underscores the scale of sustainable investment required. Sensitivity analyses further highlight the critical role of reducing green hydrogen costs to enable deep decarbonization. Overall, this study provides a robust and replicable planning tool to support policymakers in formulating strategies that align coal power sector transformation with long-term sustainability and China’s carbon neutrality commitments. Full article

The link between ESG and financial performance is still under debate. In this study, we explore which aspects of ESG specifically drive market valuations through both systematic and idiosyncratic risk channels. We analyze the impact of the three core ESG pillars, 10 subcategories, and associated controversies on market valuations in the energy sector. This analysis reveals that the environmental factor has a stronger impact (regression coefficient = 0.05) than the governance factor (regression coefficient = 0.003), emphasizing the need to prioritize environmental performance in ESG strategies. The positive coefficients for environmental resource use (0.005) and innovation (0.008) indicate that investments in efficiency and clean technologies are beneficial, while the negative coefficient for emissions (−0.004) underscores the risks associated with poor emissions management. These findings suggest that environmental risks currently outweigh governance risks for the energy sector, reinforcing the importance of aligning governance practices with environmental goals. To maximize ESG effectiveness, energy firms should focus on measurable improvements in resource efficiency, innovation, and emissions reduction and transparently communicate this progress to stakeholders. The evidence suggests that energy firms approach the ESG landscape differently, with sustainability leaders benefiting from higher valuations, particularly when ESG efforts are aligned with core competencies. However, many energy companies under-invest in value-creating environmental initiatives, focusing instead on emission management, which erodes value. While they excel in emission control, they lag in innovation, missing opportunities to enhance valuations. This underscores the potential for ESG risk analysis to improve portfolio performance, as sustainability can both create value and mitigate risks by factoring into valuation equations as both risks and opportunities. This study uniquely contributes to the ESG–financial performance literature by disentangling the specific ESG dimensions that drive market valuations in the energy sector, revealing that value is created not through emission control but through strategic alignment with eco-innovation, governance, and social responsibility. Full article

As an important part of the emerging energy portfolio, the coordinated development of the photovoltaic (PV) industry and ecological environment is a core factor in realizing the high-quality development of the energy industry. Xinjiang, located in northwestern China, possesses vast open land, abundant solar radiation, and low land-use conflict, making it a strategic hub for large-scale PV power station deployment. However, the region’s fragile ecological background is highly sensitive to land-use changes induced by PV infrastructure expansion. Therefore, scientifically evaluating the ecological impacts of PV construction is essential to support environmentally informed operation and maintenance (O&M) strategies.This study investigates the spatial distribution of PV installations and their macro-scale ecological effects across Xinjiang from 2000 to 2020. Utilizing multi-temporal satellite remote sensing data and geospatial analysis techniques on the Google Earth Engine (GEE) platform, we constructed a Remote Sensing Ecological Index (RSEI) model to quantify the long-term ecological response to PV development. It was found that PV installations were concentrated in unutilized land (37.10%) and grassland (34.45%), with the smallest proportion being found in forested land (1.68%). Nearly 70% of the PV areas showed an improving trend in the ecological environment index, and there were significantly more ecological quality-improving areas than degraded areas (69% vs. 31%). There were significant regional differences, and the highest ecological environment index was found in 2020 for the Northern Xinjiang Altay PV area (0.30), while the lowest (0.10) was observed in Hetian in southern Xinjiang. The results of this study provide a spatial optimization basis for the integration of PV development and ecological protection in Xinjiang and provide practical guidance to help the government to formulate a comprehensive management strategy of “PV + ecology”, which will help to realize the synergistic development of clean energy development and ecological safety. Full article

With climate change mitigation and carbon emission reduction as global priorities, the expansion of renewable energy has become a core strategy globally. The purpose of this study is to identify trends in key renewable energy technologies, such as solar, wind, geothermal, and water technologies, and to compare and evaluate their competitiveness across leading nations. To this end, we performed trend analyses and both patent and technology portfolio assessments employing indicators such as the number of patents granted, claim count ratio, citation ratio, and patent family ratio on 194,485 granted patents collected from 1975 to 2024, according to International Patent Classification (IPC) codes, for the five major energy powers—the United States, European Union, Japan, China, and Korea. Trend analysis revealed a sharp increase in energy-related patents from 2010, with solar technologies accounting for over 60 percent of the total. Patent portfolio results positioned the United States as the Technology Leader, leading in both activity and quality; China stood out for its quantitative expansion and Europe for its qualitative strengths. Technology portfolio findings show that, although core technologies are shared globally, application-level technologies vary by country, reflecting each nation’s industrial base, policy orientation, and technological maturity. This study delineates priority technology domains, identifies optimal R&D collaboration pathways, and recommends policy levers that accelerate commercialization—enabling policymakers and industry stakeholders to allocate resources strategically and construct balanced technology portfolios aligned with global initiatives such as carbon-neutrality targets and the RE100 commitment. Full article