Strategic Planning for Energy and the Environment

Cultural Value Conflicts in Adapting to Modern Green Public Transport Systems: An Intergenerational Study in Developing Cities

Duyen Nguyen Thi Kim — 2026-04-20

This study explores intergenerational cultural value conflicts in the adaptation to green public transport systems in Vietnamese cities, explaining the paradox between significant investment in green infrastructure and low ridership rates. Through in-depth interviews with 60 participants from three generations (X, Y, and Z) in Hanoi, Ho Chi Minh City, and Da Nang, and applying interpretative phenomenological analysis, the study finds that each generation constructs a distinct “cultural space”: Generation X perceives public transport as a “loss of control,” Generation Y engages in “cultural negotiation” between conflicting values, and Generation Z constructs a new “green face.” The results show that the metro system is accepted by all three generations, but for different reasons – luxury, modernity, sustainability – creating a rare point of convergence. The study proposes a “Tri-layered Cultural Space” model that challenges linear Western transition theories, asserting that the green transition in a transitional society is a process of “cultural accretion” rather than “replacement.” This calls for generationally-segmented policies and the design of “multi-faceted” systems that align with the cultural logic of each group.

Students’ Energy Sustainability Behaviors: Modeling the Role of Values and Self-Efficacy Beliefs

Kougias Konstantinos — 2026-04-20

Individual behavior is a multifactorial system subjected to multiple influences. This study examines the effect of personal values and self-reported efficacy of secondary school students on their energy-saving behavior. Predicting behavior is an important issue in sustainability issues, especially in energy consumption, a field that has attracted increasing interest in recent years. Data collection of 6.161 middle school students in the region of Attica have been utilized in this quantitative analysis of this paper. Ordinal regression models were applied in order to assess the ability to predict students’ energy behavior based on their personal values and self-reported efficacy. The results highlight the strong possibility students with altruistic values to engage themselves in energy-sustainable behaviors. On the contrary, those being more selfish are less likely to take actions such as turning off the lights when leaving a room or adjusting the thermostat of the heating system in order to reduce consumption. At the same time, the findings show the positive effect of perceived self-efficacy on the implementation of sustainable behaviors. Thus, emphasis is placed on the key foundations of energy behavior, specially of the future to be consumers having to decide on energy issues, unaware of the possible effects of their decision. So, the need to place more importance on the educational practices is hereby arisen targeting to the strengthening of holistic energy behavior of the students.

Toward ‘Unequal Landscapes’? Second-Home Expansion, Tourism Polarities, and Socioeconomic Disparities Revisited

Ioannis Konaxis — 2026-04-20

Empirical research examining the interrelations between settlement morphology and functional dynamics in metropolitan regions of advanced economies engages with a field that is both complex and intellectually stimulating. Within this framework, metropolitan systems in the old continent constitute a particularly compelling case, as they exhibit pronounced local specificities while simultaneously revealing (broader) structural trends and functional dynamics. These cities especially display a composite landscape in which diverse settlement morphologies intersect with intricate patterns of socio-spatial segregation. Against this background, the present study focuses on metropolitan Athens, Greece, with the objective of discussing the spatial distribution of residential (private) swimming pools in relation to a restricted set of socioeconomic aspects taken as indicators of territorial disparities. Particular attention has been devoted to evaluating claims that swimming pools act as ‘territorial markers’ of luxury consumption and ‘lock-living’ suburbanization, with implications for local class segregation and regional socio-spatial organization. More broadly, the study contributes to an empirical assessment of the evolving relationship between metropolitan expansion, demographic restructuring, and environmental sustainability in Mediterranean landscapes.

Impact of Carbon Emission Trading Policy on Green Innovation Behavior of High-energy Consuming Enterprises Based on Difference-in-Difference Model and PSM-DID

Junjun Shi — 2026-04-20

Affected by global climate change, the carbon emission trading policy, as a vital market-based environmental regulatory tool, plays a crucial role in promoting carbon reduction. To explore the impact of this policy on the green innovation quality of high-energy-consuming enterprises, this study combines the difference-in-difference model with propensity score matching to conduct an empirical analysis. Propensity score matching results show that the covariate balance between the treatment group and control group is significantly improved, verifying the reliability of the sample matching. The core explanatory variable Treat*P has a positive and significant coefficient at the 1% level, indicating that high-energy-consuming enterprises in pilot areas tend to adopt high-value green innovation strategies after implementing the Carbon Emission Trading policy. This study reveals the net effect of the carbon emission trading policy on enterprises’ green innovation behavior, provides a theoretical basis for optimizing the Carbon Emission Trading mechanism, and offers practical references for promoting the green transformation of high-energy-consuming industries.

Optimizing Gas Consumption Predictions: A Comprehensive Study of Individual and Hybrid Modeling Approaches with Practical Implications for Energy Policies

Changhao Zhang — 2026-04-20

Natural gas, the cleanest fossil fuel, is increasingly important due to its abundance and lower carbon emissions. However, accurately forecasting gas demand remains challenging. To forecast gas usage, this study uses sophisticated machine learning (ML) techniques, including CatBoost, XGBoost, and MLP. Six prediction models and hyperparameter optimization are created and assessed. Hybrid XGBoost models, particularly XGBoost-SSA and XGBoost-SMA, demonstrate superior convergence and accuracy. Visual aids like correlation matrices and scatter plots provide insights into model performance. The research contributes to enhancing the efficiency of gas distribution operations, ensuring energy security, economic stability, and environmental sustainability. By integrating renewable energy and leveraging real-time analytics, the study addresses the evolving dynamics of gas consumption forecasting, offering valuable implications for energy policies and investment strategies.

Joint Coordination Control of Hybrid Energy Storage System in New Distribution Network

Hao Bai — 2026-04-20

The current mixed energy storage (ES) system in the distribution network (DN) has become the main power system for new energy construction, but how to achieve joint optimization control of the mixed energy system in the DN is still the focus of current research. To achieve joint coordinated control of hybrid ES systems in new DNs, this study introduces a coordinated control model for ES systems based on multi-objective optimization (MOO) algorithms. The new model uses MOO algorithms to coordinate and optimize the ES system in the DN, thereby achieving accurate coordinated control of the ES system. The results show that using MOO algorithms, the network loss of the hybrid ES system is reduced by 1.258 MW, and the load disturbance was reduced by 0.24. At the same time, after using the new method, the operating cost of the hybrid ES system is reduced by about 40,000 yuan/year, and the grid losses of nodes are reduced by about 8.65%. The joint coordinated control method of the new ES system can improve the ES optimization effect of the system and reduce ES losses in the power grid. This study has good guiding significance for improving the ES efficiency of new DNs.

China’s New Energy Policies and Green Economic Development – A Quasi-Natural Experiment Based on New Energy Demonstration City Policies

Jiayue Wang — 2026-04-20

The “New Energy Demonstration City” plan plays a key role in promoting ecological modernization and achieving the “dual carbon” goals in China. This study uses data from 277 prefecture level cities between 2009 and 2022 to examine the impact of this policy on the development of green economy. We view the implementation of policies as an opportunity to approach natural experimentation, using a difference-in-differences (DID) method and controlling for the common factors that vary over time and the unique characteristics of each city. The results show that once a city is listed as a demonstration city, its green total factor productivity will significantly improve, with an average increase of approximately 0.1 units, and this effect will become stronger over time. However, the benefits of policies are not the same everywhere. The response is more pronounced in cities in the eastern region, as well as those that do not rely on mining or resource extraction for their livelihoods; However, resource dependent cities have seen almost no substantial improvement. In addition, the local technological innovation capability plays an amplifying role, and the policy effect is much stronger in cities where green patent activities are more active. These findings indicate that whether a city can successfully promote low-carbon transformation largely depends on its existing institutional foundation and innovation strength.

Modeling and Multi-objective Optimization of Carbon Emissions Throughout the Lifecycle of Zero Carbon Buildings

Xueli Yin — 2026-04-20

To manage the carbon emissions of zero carbon buildings, a building information model is used to construct a carbon emission model for zero carbon buildings, and a multi-objective optimization method based on non dominated genetic algorithm is developed to optimize the carbon emissions. The performance of the carbon emission model is analyzed using the China Energy and Carbon Emission Database (MEIC) public database, and the outcomes reveal that the data matching error rate of the model is less than 5%, and the model’s coverage of the whole span of carbon emissions reaches 87.9%. By reusing the data from Global Carbon Budget (GCB) database to predict the carbon reduction effect of the optimization plan, the outcomes reveal that the optimization plan can reduce the carbon emissions throughout the whole span by 35% to 45%, and the carbon reduction during the operation phase can reach 43.7%. From the above outcomes, the emission reduction plan based on carbon emission model and multi-objective optimization method can effectively reduce the carbon emissions. This can foster sustainable development, and provide ideas for carbon reduction plans in other fields.

Research on Aggregated Modeling of Wind Farms Considering Dynamic Coupling Characteristics and Stability Optimization in Weak Grid Environments

Zhan Zhao — 2026-04-20

The application of Grid-Forming (GFM) and Grid-Following (GFL) controllers has effectively enhanced the strength of increasingly weakened power system. However, the inherent high-order and nonlinear characteristics of wind farm models pose numerous challenges to the simulation and analysis of the dynamic stability of modern power systems.

To address these challenges, this paper proposes an innovative aggregated modeling approach for wind farms, which enables large-scale simulation and serves as a powerful tool for modern power system stability analysis. Based on the distinct Thevenin equivalent circuits of GFL and GFM units, this study introduces their respective rotor current and stator voltage weighting coefficients for the aggregation of wind turbines operating under different control modes. The constructed model can accurately represent wind farm dynamic characteristics across varying grid strengths and fault conditions. To verify the proposed model’s effectiveness, this paper compares the accuracy of the modal aggregation method against other multi-machine representation methods under Fault Ride-Through (FRT) conditions. Results demonstrate a significant reduction in errors between the proposed aggregation model and detailed model in the pre-fault, fault-on and post-fault stages. In addition, the aggregated model is utilized to investigate the port characteristics of wind farms with different GFM-GFL unit proportions. It is shown that a reasonable increase in the proportion of GFM and GFL units can significantly enhance the operational stability of wind turbines under low Short-Circuit Ratio (SCR) conditions, and effectively expand their stable operating range in weak grid environments.

Research on Distribution Transformer Layout Planning Model of Distribution Networks Considering the Impact of Distributed Generation and Electric Vehicles

Wenzhong Wang — 2026-04-20

Under the background of the “dual-carbon” strategy and the ongoing energy structure transition, the rapid penetration of distributed generation (DG) and electric vehicles (EVs) has introduced bidirectional uncertainties on both the supply and demand sides of distribution networks. To address the limitations of traditional transformer planning methods that fail to simultaneously capture the stochastic characteristics of DG and EVs, this paper proposes a multi-objective optimization model for transformer layout planning considering source-load uncertainties. The model characterizes the stochastic outputs of photovoltaic and wind power using Beta and Weibull distributions, respectively, and adopts a Monte Carlo simulation framework to represent the spatiotemporal distribution patterns of EV charging loads, thereby achieving coordinated modeling of source-load randomness. On this basis, a probabilistic optimization framework is established to minimize the total life-cycle cost-including transformer investment, network losses, and outage losses-subject to voltage, current, and capacity constraints. Simulation results on the enhanced IEEE 33-bus network verify that the proposed method can effectively improve voltage regulation, cut line losses and operating costs, and sustain system stability under substantial DG and EV penetration. The research provides a systematic modeling approach and optimization reference for transformer planning in distribution networks with high renewable energy and EV integration.

Real-time Evaluation of Relay Protection System Status for Smart Grid: A Fusion Model of Digital Twin and Deep Transfer Learning

Zhong-liang Xie — 2026-04-20

To address issues in traditional relay protection system state evaluation, such as insufficient training samples, lagging results, and manual setting management, this study proposes a real-time state evaluation model integrating digital twin and deep transfer learning. A high-fidelity digital twin system is constructed to establish bidirectional mapping and dynamic updates between the physical system and virtual twin. A sparse stacked autoencoder extracts discriminative features, and an online adaptation strategy based on deep transfer learning enables continuous self-optimization with streaming data. Experimental results show an overall accuracy of 98.2%, weighted F1-score of 0.978, and average evaluation delay reduced to 1.5 minutes. The intelligent setting management platform improves verification and download efficiency by 60% and 40% respectively, with error rate decreasing from 10.2% to 0.22%. The framework enables minute-level real-time evaluation, predictive maintenance, and closed-loop operation, providing a reliable approach for building resilient and smart grids.

Quantitative Evaluation of Thermal Conductivity Effects of Green Interior Materials on Indoor Thermal Regulation and Energy Consumption

Lu Peng — 2026-04-20

Interior finishing materials are often regarded as architectural surfaces rather than active thermal components, and their role in building energy efficiency remains underexplored. As global cooling demand continues to rise and buildings account for nearly one-third of global energy use, developing low-carbon thermal modulation strategies has become an urgent priority. In this study, seven representative green interior materials (natural wood, bamboo composite, gypsum board, diatom coating, recycled cellulose fiberboard, cork sheet, and aerogel-enhanced composite) were experimentally evaluated under controlled cooling conditions to quantify their effects on indoor heat-transfer behavior and HVAC energy consumption. Surface temperature evolution, transient heat flux, and comfort stability were continuously monitored, and thermal response curves were fitted using a first-order decay model to extract the thermal time constant τ. The results show that aerogel and cellulose finishes substantially delayed heat penetration, exhibiting τ≈ 1.47 h and τ≈ 1.32 h, respectively, representing up to 42% longer response time compared to wood. Cooling energy consumption decreased by 10–18% with low-conductivity finishes, accompanied by smoother temperature fluctuations and enhanced comfort stability. A strong correlation emerged between thermal conductivity and normalized energy demand (R2≈0.89), allowing the development of a predictive selection model for material-driven HVAC performance. These findings demonstrate that finishing layers can serve as functional thermal regulators rather than passive decorative elements, offering a scalable and lightweight strategy for reducing operational building energy and enabling low-carbon retrofit pathways.