Search Results (222)

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article

The European Health Technology Assessment (HTA) regulation (HTAR) came into effect in January 2025 and impacts the HTA process in all European Member States. Member States must give due consideration to the joint clinical assessment (JCA) report. This may require adaptations at the national level. This paper describes the anticipated changes to the Dutch national HTA process and how the Dutch National Health Care Institute (Zorginstituut Nederland, ZIN) prepared for this, because sharing experience between Member States can be of general interest for future expansion of the EU HTAR. ZIN’s implementation activities were facilitated by a project-governance structure and by a continuous gap analysis of the current national assessment and appraisal process of medicinal products, resulting in a concrete action plan. The implementation of the HTAR has two major implications for ZIN’s HTA process, namely that the scoping phase starts much earlier and that the JCA report is the starting point for the national assessment. Gaps, challenges and issues were identified in the categories: information and knowledge, IT and template, communication and stakeholder engagement, capacity and resources, and financial aspects. Based on a thorough and well-defined implementation plan, ZIN is ready to implement the HTAR in national HTA processes and to take on (co-)assessor roles for JCA of medicinal products in 2025. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

The Eastern Huang–Huai region of China is a representative mining area with a high groundwater level. High-intensity underground mining activities have not only induced land cover and land use changes (LUCC) but also significantly changed the watershed hydrological behavior. This study integrated the land use prediction model PLUS and the hydrological simulation model MIKE 21. Taking the Bahe River Watershed in Huaibei City, China, as an example, it simulated the hydrological response trends of the watershed in 2037 under different land use scenarios. The results demonstrate the following: (1) The land use predictions for each scenario exhibit significant variation. In the maximum subsidence scenario, the expansion of water areas is most pronounced. In the planning scenario, the increase in construction land is notable. Across all scenarios, the area of cultivated land decreases. (2) In the maximum subsidence scenario, the area of high-intensity waterlogging is the greatest, accounting for 31.35% of the total area of the watershed; in the planning scenario, the proportion of high-intensity waterlogged is the least, at 19.10%. (3) In the maximum subsidence scenario, owing to the water storage effect of the subsidence depression, the flood peak is conspicuously delayed and attains the maximum value of 192.3 m³/s. In the planning scenario, the land reclamation rate and ecological restoration rate of subsidence area are the highest, while the regional water storage capacity is the lowest. As a result, the total cumulative runoff is the greatest, and the peak flood value is reduced. The influence of different degrees of subsidence on the watershed hydrological behavior varies, and the coal mining subsidence area has the potential to regulate and store runoff and perform hydrological regulation. The results reveal the mechanism through which different land use scenarios influence hydrological processes, which provides a scientific basis for the territorial space planning and sustainable development of coal mining subsidence areas. Full article

This study analyzes the evolution of Pakistan’s energy policies from 1990 to 2024, documenting their transition from a singular focus on generation capacity to an integrated approach prioritizing renewable energy and efficiency. Through a systematic literature review of 110 initially screened studies, with 50 meeting the inclusion criteria and 22 selected for in-depth analysis, we evaluated policy effectiveness and identified implementation barriers. Our methodology employed predefined criteria focusing on energy efficiency, environmental sustainability, and climate impact, utilizing the Web of Science and Scopus databases. Early policies like the National Energy Conservation Policy (1992) and the Energy Policy (1994) emphasized energy security through generation capacity expansion while largely neglecting renewable sources and efficiency improvements. The policy landscape evolved in the 2000s with the introduction of renewable energy incentives and efficiency initiatives. However, persistent challenges include short-term planning, inconsistent implementation, and fossil fuels dependence. Recent framework like the Alternative and Renewable Energy Policy (2019) and the National Energy Efficiency and Conservation Plan (2020–2025) demonstrate progress toward sustainable energy practices. However, institutional, financial, and regulatory barriers continue to constrain effectiveness. We recommend that Pakistan’s energy strategy prioritize the following: (1) long-term planning horizon; (2) enhanced fiscal incentives; and (3) strengthened institutional support to meet global energy security and climate resilience standards. These measures would advance Pakistan’s sustainable energy transition while supporting both energy security and environmental objectives. Full article

This study presents a practical and scalable framework for the mid- to long-term distribution network planning that reflects real-world infrastructure constraints and investment requirements. While traditional methods often rely on simplified network models or reactive reinforcement strategies, the proposed approach introduces an investment-oriented planning model that explicitly incorporates physical elements such as duct capacity, pole availability, and installation feasibility. A linear programming (LP) formulation is adopted to determine the optimal routing and sizing of new facilities under technical constraints including voltage regulation, power balance, and substation capacity limits. To validate the model’s effectiveness, actual infrastructure and load data were used. The results show that the model can derive cost-efficient expansion strategies over a five-year horizon by prioritizing existing infrastructure use and flexibly adapting to spatial limitations. The proposed approach enables utility planners to make realistic, data-driven decisions and supports diverse scenario analyses through a modular structure. By embedding investment logic directly into the network model, this framework bridges the gap between high-level planning strategies and the engineering realities of distribution system expansion. Full article

Port green spaces are essential protective barriers, enhancing safety and environmental resilience in high-activity port regions. Given the intensity of human activities in these areas, understanding the factors influencing the carbon sequestration capacity and ecological benefits of port green spaces is crucial for developing sustainable green ports. This study integrated field investigations and remote sensing data to estimate carbon density and carbon sequestration capacity in the Dongjiakou Port area, examining their relationship with port green space planning. The results indicated that carbon density in green spaces showed a significant negative correlation with the number of lanes in adjacent roads, where an increase in lane numbers corresponded to lower carbon density. Additionally, carbon density decreased significantly with increasing distance from the shipping center. In contrast, a significant positive correlation was observed between carbon density and distance from large water bodies, indicating that green spaces closer to large water bodies exhibited smaller carbon density. Infrastructure development in Dongjiakou substantially negatively impacted vegetation carbon sequestration capacity, with effects not reversible in the short term. However, green space enhancement efforts provided additional ecological benefits, leading to a 50.9 ha increase in green space area. When assessing carbon density in urbanizing areas, geographical influences should be prioritized. Furthermore, the long-term environmental impacts of urban expansion must be considered at the early planning stages, ensuring the implementation of proactive protective measures to mitigate potential ecological disruptions. Full article

Against the backdrop of China’s urban transformation from incremental expansion to stock regeneration, community regeneration has emerged as a critical mechanism for enhancing urban governance efficacy. As fundamental units of urban systems, the regeneration of communities requires comprehensive approaches to address complex socio-spatial challenges, with public participation serving as the core driver for achieving sustainable renewal goals. However, significant regional disparities persist in the effectiveness of public participation across China, necessitating the systematic institutionalization of participatory practices. Guangzhou, as a pioneering city in institutional innovation and the practical exploration of urban regeneration, provides a representative case for examining the evolutionary trajectory of participatory planning. This research employs Arnstein’s Ladder of Participation theory, utilizing literature analysis and comparative case studies to investigate the evolution of participatory mechanisms in Guangzhou’s community regeneration over four decades. The study systematically examined the transformation of public engagement models across multiple dimensions, including organizational frameworks of participation, participatory effectiveness, diversified financing models, and the innovation of policy instruments. Three paradigm shifts were identified: the (1) transition of participants from “passive responders” to “active constructors”, (2) advancement of engagement phases from “fragmented intervention” to “whole-cycle empowerment”, and (3) evolution of participation methods from “unidirectional communication” to “collaborative co-governance”. It identifies four drivers of participatory effectiveness: policy frameworks, financing mechanisms, mediator cultivation, and engagement platforms. To enhance public engagement efficacy, the research proposes the following: (1) a resilient policy adaptation mechanism enabling dynamic responses to multi-stakeholder demands, (2) a diversified financing framework establishing a “government guidance + market operation + resident contribution” cost-sharing model, (3) a professional support system integrating “localization + specialization” capacities, and (4) enhanced digital empowerment and institutional innovation in participatory platform development. These mechanisms collectively form an evolutionary pathway from “symbolic participation” to “substantive co-creation” in urban regeneration governance. Full article

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security. Full article

Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to insufficient DRL training samples, which easily triggers the problems of underfitting and insufficient exploration of the decision space and thus reduces the accuracy of the scheduling plan. In addition, conventional data-driven methods are also difficult to accurately predict renewable energy output due to insufficient training data, which further affects the scheduling effect. Therefore, this paper proposes a small-sample scenario optimization scheduling method based on multidimensional data expansion. Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed method is verified by arithmetic examples. Full article

The “carbon neutral power generation” policy of the European Union requires the phasing out of fossil fuel power plants. These plants still play a crucial role in the energy mix in many countries; therefore, efforts are put forward to lower their CO₂ emissions. The available solution for an existing coal plant is the implementation of biomass co-firing, which allows it to reduce twice its carbon footprint in order to achieve the level of natural gas plants, which are preferable on the way to zero-emission power generation. However the side effect is a significant increase in the bulk fuel volumes that are acquired, handled, and finally supplied to the power plant units. A necessary extension of the complex logistic system for unloading, quality tagging, storing, and transporting biomass may increase the plant’s noise emissions beyond the allowed thresholds. For a comprehensive assessment of the concept of expanding the power plant’s biofuel supply system (BSS), a discrete simulation model was built to dimension system elements and verify the overall correctness of the proposed solutions. Then, a dedicated noise emission model was built for the purposes of mandatory environmental impact assessment procedures for the planned expansion of the BSS. The noise model showed the possibility of exceeding the permissible noise levels at night in selected locations. The new simulations of the BSS model were used to analyze various scenarios of biomass supply with regard to alternative switching off the selected branches of the whole BSS. The length of the queue of unloaded freight trains delivering an average quality biomass after a period of 2 weeks is used as a key performance parameter of the BSS. A queue shorter than 1 freight train is accepted. Assuming the rising share of RESS in the Polish energy mix, the thermal plant’s 2-week average power output shall not exceed 70% of its maximum capacity. The results of the simulations indicate that under these constraints, the biofuel supplies can be sufficient regardless of the nighttime stops, if 50% of the supplied biomass volumes are delivered by trucks. If the trucks’ share drops to 25%, the plant’s 2-week average power output is limited to 45% of its maximum power. The use of digital spatial simulation models for a complex, cyclical-continuous transport system to control its operation is an effective method of addressing environmental conflicts at the design stage of the extension of industrial installations in urbanized areas. Full article

Research was conducted in Gannan Prefecture, China, to better understand the characteristics of carbon emissions and sequestration in areas dominated by animal husbandry. The emission factor method was used to calculate and analyze changes in carbon emissions from 2009 to 2024. The region’s average annual carbon emissions from animal husbandry are 774,286 t C-eq (2,839,049 t CO₂eq), with enteric emissions from cattle being the biggest contributor. However, as the number of locally raised cattle and sheep has decreased, carbon emissions have gradually fallen at an average annual rate of −1.0%. The annual average total carbon sequestration of vegetation in the region is 6,861,535 t C-eq, and the carbon content in underground biomass is higher than that in aboveground biomass, making it the main contributor to grassland carbon sequestration. Carbon sequestration from grassland vegetation is greater than the carbon emissions from animal husbandry, which means that the entire production system is currently a carbon sink. Meanwhile, the analysis of land-use carbon sequestration found that the annual average total sequestration by forests and grasslands over the same time period was 752,327 t C-eq, and sequestration is increasing at an annual rate of 1.4%, primarily driven by the progressive expansion of forested areas. Although the regional carbon emissions from animal husbandry are lower than the carbon sequestration, developing a science-based animal husbandry plan aligned with regional ecological thresholds, continuing to implement grass–livestock balance management measures, and preventing livestock numbers from exceeding their ecological carrying capacity remain critical to promoting sustainable coordination between livestock economies and ecological conservation. Full article

Based on the regional disaster system theory, this study constructed a comprehensive flood risk indicator system for small reservoirs, covering the entire flood disaster process from three dimensions: hazard, vulnerability, and exposure. The analytic hierarchy process (AHP) and entropy weight method (EW) were used to determine indicator weights, and a risk assessment was conducted for small reservoirs in Huangshan City, Anhui Province, China. The results indicate that most reservoirs exhibit medium–low overall risk, yet distinct localized high-risk zones exist. High-economic-density areas such as Tunxi District, the central–eastern parts of Huangshan District, and the central and eastern parts of Qimen County have become high-risk clusters due to prominent exposure indicators (numbers of villages and medical facilities). Reservoirs in western and northern regions exhibit higher hazard levels, primarily driven by rainfall and catchment areas. Dam height and reservoir capacity are the main factors affecting vulnerability, with no significant spatial clustering for high-vulnerability reservoirs. Through the decoupling of three-dimensional indicators, this study reveals the differentiated driving mechanisms of “hazard–vulnerability–exposure,” providing a scientific basis for optimizing flood control engineering (e.g., reservoir capacity expansion, dam reinforcement) and risk zoning management (e.g., emergency evacuation planning in high-exposure areas) for small reservoirs. Full article

To achieve carbon neutrality targets in the power sector, regions with rich coal and renewable energy resources are facing unprecedented pressure. This paper explores the decarbonization pathway in the power sector in Inner Mongolia, China, under different energy transition scenarios based on the Long-Range Energy Alternatives Planning System (LEAP) model. This includes renewable energy expansion, carbon capture and storage (CCS) applications, demand response, and economic regulation scenarios. Subsequently, a combination of the Logarithmic Mean Divisia Index (LMDI) and Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model was developed to investigate the influencing factors and power generation efficiency in low-carbon electricity. The results revealed that this region emphasizes first developing renewable energy and improving the carbon and green electricity market and then accelerating CCS technology. Its carbon emissions are among the lowest, at about 77.29 million tons, but the cost could reach CNY 229.8 billion in 2060. We also found that the influencing factors of carbon productivity, low-carbon electricity structures, and carbon emissions significantly affected low-carbon electricity generation; their cumulative contribution rate is 367–588%, 155–399%, and −189–−737%, respectively. Regarding low-carbon electricity efficiency, the demand response scenario is the lowest at about 0.71; other scenarios show similar efficiency values. This value could be improved by optimizing the energy consumption structure and the installed capacity configuration. Full article

As one of Europe’s rare floodplain forest ecosystems, the İğneada Longos Forests face increasing ecological pressures; this study examines land use and land cover (LULC) changes in the İğneada Longos Forests, a protected national park in Turkey, between 1984 and 2014, while also assessing future climate change impacts under different shared socioeconomic pathways (SSPs). In this context, the MaxEnt model, which exhibits a very high sensitivity, was used to determine the land use/land change and the change in natural distribution habitats of the forest tree species in the İğneada Longos Forests, which constitute the research area, due to the effects of climate change. The analysis of forest management plans revealed significant LULC shifts, including wetland loss, cropland expansion, and declines in pioneer tree species, such as the lowland maple and the European ash, due to anthropogenic pressures and increasing droughts. Climate modeling using the Emberger and De Martonne indices projected severe aridity by 2100, with Mediterranean climate dominance expanding (up to 89.25% under SSP3–7.0) and humid zones disappearing. These changes threaten biodiversity, carbon sequestration capacity, and ecosystem stability, particularly in floodplain forests, which are critical for carbon storage. The findings underscore the urgent need for adaptive conservation strategies, stakeholder collaboration, and climate-resilient forest management to mitigate ecological degradation and sustain ecosystem services under escalating climate stress. Full article