Sustainable Energy Practices Research Articles

Achieving behavioral intention to renewable energy through perceived costs and benefits and environmental concern

Due to the high pollution caused by the consumption of energy from fossil fuels, governments have implemented policies that allow the transition to renewable sources, but this depends on many factors associated with the costs and attitudes of the population. This study examines the influence of perceived costs, benefits, and environmental concerns on the behavioral intentions toward renewable energy (RE) adoption in Ciudad Juarez, Mexico. A structural equation model is used to validate five hypotheses and information from 511 responses to a survey to explore how perceived costs (PCO) as an independent variable, perceived benefits (PBE), and environmental concerns (ECO) as mediators support intentions to adopt solar energy technologies. Findings reveal that PCO impacts PBE and ECO directly and influences behavioral intentions (BIN) indirectly. This underscores the importance of analyzing PCO in the context of developing countries due to the low per capita income. The study identifies ECO as the most significant factor affecting BIN and PBE as the primary influencer of ECO, suggesting that promoting the benefits of RE could enhance environmental concerns and, consequently, adoption intentions. Sensitivity analysis indicates the necessity of effectively disseminating cost-related information, as costs often overshadow perceived benefits. The findings advocate for clear communication regarding RE costs and benefits and the formulation of supportive policies to facilitate RE adoption in Mexico, highlighting the crucial role of comprehensive awareness strategies in improving public perception and acceptance of RE technologies, providing valuable insights for policymakers, stakeholders, and educators to promote sustainable energy practices.

Sustainable energy practices and cryptocurrency market behavior

We examine the effects of energy usage on the return, volatility and jump processes in the Bitcoin (BTC) and Ethereum (ETH) markets. Our main finding indicates that while BTC returns respond significantly to changes in electricity consumption, the effect of electricity consumption on ETH returns is negligible. We attribute this discrepancy to BTC's relative energy inefficiency, which contrasts with ETH's commitment to transitioning to a more energy-efficient mining protocol over our sample period. Additionally, we show that the effect of electricity consumption on cryptocurrencies is mitigated by media coverage and policy uncertainty regarding the state of the economy, environment, and cryptocurrency markets. Our results suggest that when trading cryptocurrencies, investors consider their relative energy efficiency and price electricity consumption differently, highlighting the influence of the transition towards more sustainable energy practices on investor preferences and investment decisions in cryptocurrency markets.

Developing a global sustainable electricity use index using the pressure-state-response framework

This study analyse and compare the sustainable electricity usage in 60 countries listed on the official websites of World Energy Consumption Statistics and Climate Bond Initiative. The study also analyses the impact of increased usage of sustainable electricity on the economies' dependence on non-renewable energy sources in the evaluation system. We used a standard index system based on the Pressure-State Response (PSR) model to measure global sustainable electricity usage. Model results convey that Norway is the best performer in sustainable electricity usage, while several European countries display commendable scores, confirming their commitment to sustainable electricity practices. On the other hand, despite being the leading economies in terms of GDP, major economies such as the United States, China, Japan, and India have underperformed compared to others in the evaluation system. The study employs regression techniques to explain the relationship between sustainable electricity usage and non-renewable energy dependence. Results confirm a negative relationship between the variables, indicating the role of sustainable energy practices in reducing fossil fuel consumption. It emphasizes the urgency of a balanced approach to economic growth and natural resource usage to support a green future.

Multilayer asset association network, green technical cooperation innovation, and default risk: Evidence from China's high energy-consuming industries

High energy-consuming industries are expected to play a significant role in energy conservation and emissions reduction. This study collects data on China's high energy-consuming listed companies from 2009 to 2022. Using the complex network theory, we construct a multilayer asset association network for high energy-consuming enterprises and examine how different layers and totality affect default risk. The results revealed that node-weighted degree centrality in the asset association networks of high energy-consuming enterprises, particularly in the guarantee association network, significantly increased default risk. Further mechanistic analysis reveals that the level of green technical cooperation innovation mitigates the impact of increased weighted degree centrality on the default risk of high energy-consuming enterprises. For high energy-consuming enterprises located in the eastern region, central region or non-heavily polluting industries, the influence of weighted degree centrality in the asset association network on default risk is more significant. This study enriches complex network research and provides guidance for managing default risk in high energy-consuming enterprises. Additionally, these studies align with global trends toward enhanced asset transparency, sustainability, and financial resilience, and contribute emerging markets to the transition toward more sustainable energy practices.

Analysing Oil Prices Fluctuations, Carbon Emissions and Renewable Energy Trends in Uncovering the Eco‐Economic Interplay in China's Trade Liberalization

ABSTRACTThis study evaluates the factors contributing to carbon emissions in China between 2002 and 2021, considering the impact of GDP growth, shocks in oil prices, trade liberalization and energy use. Using the dynamic simulated ARDL (SARDL) model, we indicate a long‐term link between trade liberalization, oil prices (WTI) shocks, GDP growth, energy use and CO2. The novel SARDL revealed a direct relationship between trade liberalization and CO2 in China in the long term. However, the results show a negative impact of WTI shocks on CO2 emissions. The study suggests that renewable energy (RE) significantly and negatively affects China's CO2 emissions. In addition, the findings conclude that, in the case of China, GDP has an insignificant long‐run relationship with CO2 emissions. The outcomes provide valuable insights for policymakers, researchers and industry stakeholders to enhance RE and promote sustainable energy practices.

Energy transition for sustainable economy: What is the role of government governance and public concern?

The global imperative for sustainable development necessitates a deeper understanding of the factors driving energy transitions. This study examines how government environmental governance and public environmental awareness influence China's shift towards sustainable energy practices. Utilizing a comprehensive dataset from Chinese prefecture-level cities, we analyze the impacts of governmental environmental governance and public concern on energy transition intensity across regions. Our findings reveal that both proactive government investment and heightened public awareness significantly contribute to advancing energy transitions, with notable regional variations. The analysis uncovers complex relationships between local economic conditions, societal engagement, and transition progress, highlighting the need for tailored strategies. These insights contribute to the sustainable development discourse by elucidating the synergistic effects of governance and public engagement in driving energy transitions. Our research offers valuable policy implications for fostering sustainable energy futures, emphasizing the importance of coordinated efforts between governmental structures and public sentiment in achieving meaningful environmental progress.

Enhancing climate action in OECD countries: the role of environmental policy stringency for energy transitioning to a sustainable environment

Climate change affects the world economy, environment, and human well-being, jeopardizing overall sustainability. The escalating impacts of climate change emphasize the necessity to assess the moderating influence of environmental policy stringency (EPS) on the association of energy transition (ET) and GHG emissions from 1990 to 2020 across 36 OECD countries. Further, this study incorporates the direct impact of energy transition (ET), environmental-related technology (ERTs), green innovation (INV), and Gross Domestic Product (GDP) on GHG emission. For this purpose, study employs an extensive range of econometric techniques, including DOLS, FMOLS, CCR, and MMQR approaches to evaluate data attributes. The findings of MMQR demonstrate that interaction of ET*EPS contributes to lower GHG emissions from −0.271% to −0.300% across all quantiles (20th to 80th). This indicates that the implementation of environmental policies fosters adoption of energy transitions to mitigate the negative effects of climate change, particularly to reduce GHG emissions. Further, environmental-related technologies (ERTs) and green innovation (INV) decrease GHG emissions by 0.15%–0.13% and 0.967%–2.049%, respectively, across all quantiles, thus encouraging environmental sustainability. The heterogeneous effect of ERTs is due to varying levels of adoption of environmental technologies in sample countries. The findings highlight the crucial need for integrating environmental policy strictness and energy transition measures to effectively mitigate GHG emissions. It highlights the significance of adaptive, responsive policies that are in line with SDGs 7 & 13, which concentrate on sustainable energy practices and integrated climate action in OECD economies.Graphical

Environmental impact assessment of different power generation strategies in Oman: A comparative life-cycle analysis

This paper presents a comparative environmental impact assessment considering different power generation strategies in Oman. The Life-Cycle Assessment (LCA) methodology, and the OpenLCA tool, were employed in carrying out comprehensive analysis, and evaluation of the various environmental aspects in filling the research gap, by replacing conventional diesel generators with natural gas alternatives in Oman. The obtained results indicate that utilizing natural gas significantly reduces environmental impacts, including a decrease in global warming potential to 2.27 million kg CO2 eq, fossil fuel depletion to 34.5 million kg oil-eq, and ozone depletion to 0.13 kg CFC-11 eq. This study would help in policy decision-making to support a potential shift in power generation system, transitioning from the current use of diesel generator sets to flared produced Natural Gas across crude oil processing plants. Thus, mitigating operational cost, and improving efficiencies, for sustainable developments. Another salient part of this study is the implementation of more sustainable energy practices that supports the broader application of LCA in evaluating industrial environmental impacts. Furthermore, the results obtained in this work are in line with recent global climate change commitments and Oman's Vision 2040, in achieving cleaner energy sources to minimize environmental harm in the oil and gas sector.

Potential Utilization of Tamblang Dam for Micro Hydro Power Plant to Increase Renewable Energy Mix for Indonesia

Abstract Bali, a prominent tourist destination in Indonesia, faces an increasing demand for electricity, primarily supplied by non-renewable sources. With a target to increase renewable energy mix, Bali focuses on leveraging its water resources, notably Tamblang Dam, for hydroelectric power generation. This research aims to determine the potential utilization of Tamblang Dam as a micro hydro power plant, highlighting its capacity to significantly contribute to renewable energy mix in Indonesia generally and Bali particularly. Analysis reveals a potential electricity generation of 0.52 MW, with an annual hydro power production estimate of 4.57 GWh. Considering discharge availability, height of falling water and efficiency of hydro power system. The research also underscores the project’s economic viability and profitability, emphasizing its role in fostering sustainable energy practices in the region.

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

The role of value, belief and norm in shaping intentions to use residential rooftop solar for environment sustainability

The ongoing climate crisis presents a formidable global challenge, driving the urgent need for both technological advancements and a deeper understanding of the psychological factors that influence sustainable energy practices. Among the various technologies available, solar photovoltaic (PV) systems are pivotal in reducing greenhouse gas emissions. However, the adoption of residential rooftop solar systems, particularly from a psychological perspective, remains underexplored. Hence, this study aims to investigate the influence of personal values, beliefs, and norms on the intention to adopt residential rooftop solar systems. By integrating the Value-Belief-Norm (VBN) framework with Norm Activation Theory (NAT) and the norm internalization process, the research seeks to provide a more nuanced understanding of the environmental and sustainability context. This study employs a non-probability purposive sampling method, collecting 303 valid responses from individuals who do not currently use residential rooftop solar systems, live in landed houses, and are involved in primary household decision-making. Data were analyzed using SmartPLS software. The findings confirm all hypothesized relationships, except for the impact of egoistic values on both the new ecological paradigm and awareness of consequences, as well as the influence of descriptive norms on personal norms. This paper introduces a novel approach by adapting the VBN model to predict the intention to use residential rooftop solar systems through several key modifications: integrating ecological values and openness to change, establishing connections between different values and beliefs (such as altruistic, biospheric, egoistic, openness to change, and ecological values) and awareness of consequences, and incorporating the influence of response efficacy, injunctive social norms, and descriptive norms in positively shaping personal norms.

Pile driving noise impacts behavioral patterns of important East Asian juvenile marine fishes

The surge in renewable energy demand, particularly from offshore wind farm (OWF), raises concerns about underwater noise pollution during their construction. Despite extensive studies on underwater noise impacts in European and Western countries, local species targeted investigations are crucial for global application. We assessed how pile driving noise during OWF construction affected the behavior of three juvenile Korean fishes (Lateolabrax japonicus, Acanthopagrus schlegelii, and Platichthys stellatus) prevalent in East Asian seas. Pile driving noise playback induced rapid changes in L. japonicus and A. schlegelii swimming speed and group cohesion, with 40 % remaining un-habituated after 5 min. Notably, L. japonicus showed clear avoidance of the noise source, while P. stellatus behavior remained unchanged at all. Our findings confirm that even relatively low-intensity impulsive noise can alter fish behavior, potentially due to species-specific characteristics. Accumulated data will inform the development of strategies for smart OWF establishment, advancing sustainable energy practices.

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Differentiable programming for gradient-based control and optimization in physical systems

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

Sustainable Energy Practices in Thailand and Japan: A Comparative Analysis

Sustainable Energy Practices in Thailand and Japan: A Comparative Analysis

Mapping biomimicry research to sustainable development goals

This study systematically evaluates biomimicry research within the context of sustainable development goals (SDGs) to discern the interdisciplinary interplay between biomimicry and SDGs. The alignment of biomimicry with key SDGs showcases its interdisciplinary nature and potential to offer solutions across the health, sustainability, and energy sectors. This study identified two primary thematic clusters. The first thematic cluster focused on health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's role in healthcare innovations, sustainable collaboration, and land management. This cluster demonstrates the potential of biomimicry to contribute to medical technologies, emphasizing the need for cross-sectoral partnerships and ecosystem preservation. The second thematic cluster revolves around clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), showcasing nature-inspired solutions for sustainable development challenges, including energy generation and water purification. The prominence of SDG 7 within this cluster indicates that biomimicry significantly contributes to sustainable energy practices. The analysis of thematic clusters further revealed the broad applicability of biomimicry and its role in enhancing sustainable energy access and promoting ecosystem conservation. Emerging research topics, such as metaheuristics, nanogenerators, exosomes, and bioprinting, indicate a dynamic field poised for significant advancements. By mapping the connections between biomimicry and SDGs, this study provides a comprehensive overview of the field's trajectory, emphasizing its importance in advancing global sustainability efforts.

Effects of solar energy absorbed by south wall on carbon emission from space heating in hot summer-cold winter region

Carbon emission caused by space heating (CEH) in hot summer and cold winter climate zone has increased dramatically. Taking advantage of the solar energy absorbed by south wall (SEASW) can effectively reduce CEH, however, the accurate quantification of its impact remains a challenge. The study investigates the potential of SEASW in mitigating CEH. Key parameters affecting CEH, such as sunny day ratio, insulation form, and outer surface heat transfer coefficient of south wall (h), were analyzed. Results show the reduction in carbon emissions caused by the solar energy absorbed by each unit area of the south-facing wall (ΔCreduction) can be as high as 18.61 kgCO2⋅m−2 for the wall employing internal insulation and smaller h. A prediction model of ΔCreduction was further developed by using multivariate nonlinear regression, which can estimate the reduction in CEH for different fuels as heating source by a south wall in different sizes. Besides, the potential of SEASW on the reduction of carbon emission per unit floor area can reach 12.2 %. This study underscores the importance of SEASW as a carbon-neutral heating strategy, particularly for regions characterized by short winter heating periods and lower heating loads compared to colder climates. It provides valuable insights for sustainable energy practices and policy formulation.

Occupants’ willingness to reduce in-room energy consumption − a discrete choice experiment (DCE) approach with occupants in a student apartment

This study pioneers a comprehensive methodology to quantify occupants’ willingness to reduce energy consumption behaviors, specifically focusing on student occupants within a student apartment under an all-inclusive payment rental scheme. Employing a dual-component approach, the study combines self-indicated energy consumption behavior attributes with the financial rebate occupants are willing to accept, shedding light on the intricate factors influencing their decisions regarding energy consumption. The Discrete Choice Experiment (DCE) serves as a novel tool to identify and measure the financial value, conceptualized as Willingness-to-Accept (WTA), for specific energy-saving behaviors, contributing to a nuanced understanding of the economic valuation placed on sustainable practices. A pre-survey and a DCE-based survey were conducted within the student apartment, the former one comprehending general energy consumption behaviour patterns and rebate preferences, and the latter examining the impact of financial incentives on behavioral changes, providing a comprehensive and longitudinal perspective. The findings highlight the paramount influence of factors such as “air conditioning setpoint” on occupants’ decisions followed by “lighting time” and “shower time”, emphasizing the delicate balance between occupant comfort, financial considerations, and environmental consciousness. Despite limitations related to the specificity of the study context and potential discrepancies between hypothetical and real-world behavior, this research offers practical implications for designing effective energy-saving interventions tailored to the unique characteristics of student occupants. Policymakers and stakeholders can leverage these insights to inform incentive structures, energy conservation initiatives, or pricing strategies that align with consumer preferences, ultimately encouraging sustainable energy practices in residential settings.

The influence of grid connectivity, electricity pricing, policy-driven power incentives, and carbon emissions on renewable energy adoption: Exploring key factors

In the modern era, surging economic activities heighten energy demand, depleting traditional reserves and harming the environment. Renewable energy emerges as a vital alternative but faces technical, economic, and policy challenges. This study explores the impact of factors like energy grid, government incentives, CO2 emissions, economic growth, and electricity prices on renewable energy adoption in Pakistan from 1990Q1 to 2022Q4. Results reveal a U-shaped relationship between the energy grid and renewable energy penetration in the short and long terms. National support initially boosts green energy but diminishes over time. Economic incentives initially hinder adoption (i.e., 7.445, p < 0.000) but increase in the long run (i.e., −2.881, p < 0.000). Energy prices suppress adoption in the short term (i.e., −0.084, p < 0.050). Income correlates positively in the short term (i.e., 0.006, p < 0.000) but negatively in the long term (i.e., 0.019, p < 0.000). Policy-induced incentives and energy prices drive adoption, with impacts on income observed. Causal relationships and variance decomposition analysis highlight key factors influencing renewable energy integration in Pakistan, thereby informing policy and strategic decisions to foster sustainable energy practices.