Energy, Sustainability and Society最新文献

Background

The European Union has established a strategic objective to attain carbon neutrality across the continent by the year 2050; however, this complex undertaking is shaped by a variety of influencing factors. It is particularly important to monitor the effects of such a long-term strategy, as it will influence all aspects of the European Union’s sustainable energy development as well as the welfare of its citizens. Since no universally accepted methodology exists for tracking the effects of decarbonization, the use of machine learning as a method of artificial intelligence is proposed—not only to generate concrete results but also to evaluate its applicability for this purpose. The main objective of this research is to assess the trends of 13 selected energy indicators that are vital to the decarbonization initiative. The research was conducted on a sample of 27 countries for the period from 2013 to 2030 using a novel predictive model developed in the Python runtime environment.

Results

The primary findings of the research indicate that the EU is likely to experience significant fluctuations in the values of specific indicators. The anticipated progressive rise in electricity prices is expected across all EU countries, accompanied by an increase in consumption. In addition, the projected growth in energy imports presents a significant challenge that will affect the competitiveness of the European economy and the social standing of its citizens. Particularly disadvantaged in the implementation of the decarbonization strategy will be landlocked countries that are highly dependent on energy imports and therefore vulnerable to fluctuations in prices and security of supply. Also at risk are countries facing difficulties in the deployment and exploitation of renewable energy sources, as well as those with weaker socioeconomic indicators. The results further indicate a rising risk to energy security, even in the wealthiest EU countries. Overall, the projections suggest an increase in CO₂ levels up to 2030, followed by a gradual decline thereafter. A particular challenge for managing the decarbonization strategy lies in the significant fluctuations of the monitored parameters, which hinder planning in every respect.

Conclusions

In light of the geopolitical and supply chain shifts post-2022, it is clear that a comprehensive reassessment of the strategies for managing the decarbonization of the European Union economy is necessary. The research findings demonstrated the effectiveness of the proposed machine learning approach, which has potential for enhancement due to its scalability and adaptability. The study provides governance and methodological recommendations.

Background

The European Union considers the decarbonization of Europe by 2050 a strategic objective. This necessitates finding the solutions that will support the intricate process of formulating energy policies and decisions with enduring implications for the economy, environment, and social welfare of European Union citizens and beyond. The primary objective of this study is to evaluate the suitability of an artificial intelligence (AI)-based approach in policymaking (control stage) with the aim of achieving a more optimal formulation of the common European energy policy and the policies of individual member states.

Results

Given the scope and volatility of the data, as well as the research objective, data processing was conducted using clustering—a technique within artificial intelligence (AI)—which is suitable for producing more precise, explainable, and provable (PEP) outcomes. In doing so, the study addresses one of the main obstacles to the broader use of AI in policymaking: the current lack of trust in AI-based solutions. The research was conducted in three stages. In the first stage, energy security indicators were determined based on 13 selected indicators using the aggregate approach. In the second stage, clustering was executed as an unsupervised machine learning method, utilizing the K-means algorithm as the designated learning model. In the third stage, a classifier model utilizing an artificial neural network was proposed. The research findings have revealed that countries exhibiting the highest levels of energy security, and consequently the most favorable conditions for sustainable growth, have different energy portfolios, unique economic frameworks, and differing energy prices.

Conclusions

The research findings are significant in the domains of energy and environmental policies, decision theory, and AI (with special emphasis on the EU AI Act). The research highlights the efficacy of an interdisciplinary approach and contributes to the studies about the use of AI in policymaking, with emphasis on the improvements that will lead to its greater power and precision—one of the milestones for efficient policymaking. Policymaking based on PEP AI outcomes can be seen as one of the most efficient methods for strategic planning and control of decarbonization of Europe; therefore, the paper also proposes recommendations in this context.

Background

Photovoltaic (PV) systems are nowadays a central pillar in the expansion of renewable energy in Germany. Nevertheless, further significant growth in renewable energy will be needed in the future to meet the national emission reduction targets set by the German government. Homeowners play a crucial role in the expansion of PV capacity. In a discrete choice experiment, we empirically examine the impact of a large number of dimensions on homeowners’ PV adoption, including attributes that have received less attention in the literature so far, such as the included (smart energy) services, government subsidies, and forms of financing.

Results

Our results show that increasing levels of smart energy services for PV systems increase respondents’ valuation of smart energy services, while increasing the access rights of the contractual partner has a negative impact on the valuation. The latter negative effect is mitigated by an element of co-determination. Furthermore, our results point to the importance of government subsidies as a measure to increase PV adoption. Participants preferred one-time (or continuous) payments over continuous (or one-time) payments when government grants amounted to 40% (or 10%) of the investment cost; they were indifferent between both forms when subsidies amounted to 20% of the investment cost. Homeowners clearly preferred loan financing to self-financing only at an effective interest rate of 1.03%, as opposed to 3.53%. This result indicates a limited effectiveness of this subsidy measure, which is designed to overcome the problem of high investment costs from a conceptual point of view. Our results also show that homeowners are not so heterogeneous when it comes to the importance they attach to certain attributes related to PV adoption. Decisions were made independent of socio-demographic characteristics, but are related in some cases to the homeowners’ value orientations and risk inclination.

Conclusions

Homeowners are more likely to adopt smart energy services when they are involved in the typically automated processes through decision prompts. Financial factors are of pivotal role. There is a need to tailor financing strategies, as preferences for subsidy schemes vary with the level of financing. In addition, low-interest loans are ineffective in reducing the high upfront costs of PV deployment. Smart energy services have great potential, but there are also some caveats.

Background

In the recent years, owing to the increasing carbon emissions from anthropogenic activities, the challenges caused by global climate change, including the greenhouse effect, sea-level rise, and extreme weather events, have become increasingly severe. It is also an urgent task for many countries to develop clean energy, reduce carbon emissions, and establish a green low-carbon development structure. As a renewable and environmentally friendly energy source, tidal power is abundant in numerous coastal regions. Constructing tidal power plants to harness this renewable energy source not only provides substantial energy benefits but also plays a pivotal role in advancing green and sustainable development. Moreover, tidal energy has profound implications for societal transformation, fostering economic growth, and enhancing stability. Therefore, tidal energy is an indispensable component of clean power generation, paving the way for a more sustainable and equitable world.

Main text

By summarising the ongoing research on tidal energy, this paper offers a comprehensive exploration of the current status of tidal energy development and crucial insights derived from tidal energy applications. The mechanism of tidal energy and the structural design of tidal power stations are systematically explained, and the characteristics of tidal energy use to generate electricity in different regions are introduced. Focussing on China in combination with other countries, the latest technological achievements are summarised, and corresponding improvement measures are proposed for tidal energy development and implementation.

Conclusions

Tidal energy, characterised by zero-emission attributes, renewability, and operational reliability, offers a vital pathway towards sustainable energy systems. Despite mature technology with decades of commercial operation, its deployment has progressed slowly because of persistent challenges, including high capital costs and ecological impacts on marine ecosystems. Consequently, resolving these constraints necessitates notable advancements in policy frameworks and technological innovation. This paper could provide reference material for the increased popularization and sustainable development of tidal energy power generation technology.

Background

Emerging energy technologies offer significant opportunities for climate change mitigation. However, the assessment of their potential environmental impact through prospective life cycle assessment (pLCA) is challenging owing to parameter uncertainties arising from data gaps, temporal variability, and evolving technological contexts when modeling their prospective life cycle inventories (pLCI). Existing methodologies lack standardized approaches for systematically integrating parameter uncertainty within pLCI frameworks, often initially overlooking it. In order to fill this gap, this study proposes a structured and transparent approach for incorporating parameter uncertainty directly into the pLCI modeling process. The goal is to enhance the robustness, transparency and reproducibility of pLCI models.

Results

A decision–support flowchart based on an adapted six-step framework was developed to help life cycle assessment (LCA) practitioners address parameter uncertainty during the “goal and scope definition” and “life cycle inventory” phases of pLCA. The flowchart guides users through the process of defining of the assessment’s goal, scope, as well as its temporal and geographical boundaries, and the technology’s maturity level (Step 1). Step 2 entails gathering data to depict the technology’s development. Steps 3 and 4 involve identifying parameters that are likely to change in the future, such as manufacturing processes, materials, equipment and component dimensions, as well as their respective uncertainties. Step 5 includes the learning effects required for industrial-scale production once the technology has reached maturity. Finally, step 6 identifies external developments impacting the technology, as well as contributing uncertainties. A case study of a fuel cell-based propulsion system for a hydrogen-powered aircraft in 2040 illustrates the applicability of the framework.

Conclusions

This study introduces a structured flowchart to support decision making in cases when parameter uncertainty should be integrated into pLCI modeling. By supporting the selection of appropriate prospective methods as well as uncertainty identification and characterization strategies, the proposed flowchart enhances the transparency, consistency, and representativeness of the pLCA results, facilitating their broader application in emerging technology assessment methods.

Background

Sustainability assessment comprises many different forms of assessment—from Life Cycle Sustainability Assessment to freely chosen indicator assessments—often yielding contradictory results. Multi-criteria decision-analysis (MCDA) methods have been recognized as a powerful and frequently applied tool to support decision-making in the field of energy. This study analyzes the application of MCDA in the sustainability assessment of energy technologies and systems within the Helmholtz Association, a network of German research centers addressing important topics ranging from cancer research to polar science. Energy technologies are a key focus of research within several Helmholtz research centers. Based on 20 case studies performed by Helmholtz researchers, we identify trends, challenges, and opportunities in criteria selection, MCDA method application, and stakeholder engagement.

Results

The selection of criteria and indicators often reflects the triple bottom line framework, with a strong emphasis on environmental and economic dimensions, while social criteria receive little attention due to methodological gaps. For indicator aggregation, there were three preferred methods: the Weighted Sum Method (WSM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for compensatory studies due to its ease of application and simplicity, and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) due to its non-compensatory attributes, consistent with the principles of strong sustainability. However, inconsistencies in weight elicitation methods, with frequent misalignment between the chosen methods and underlying MCDA principles, were found in the analyzed studies. The integration of stakeholders remains underutilized, with most studies involving experts but lacking broader societal involvement. Participatory techniques such as workshops and surveys are mainly applied for criteria weighting, but their implementation across all MCDA stages remains limited. Analysis of group decision-making approaches indicates a predominance of input-level aggregation, with few studies exploring comparative or output-level techniques.

Conclusions

This paper highlights the need for methodological advancements in social sustainability assessments and more robust stakeholder engagement strategies. In addition, further education on MCDA methods is needed to bridge the knowledge gaps of practitioners. By comparing Helmholtz MCDA practices with best practices from other research, this work aims to strengthen the sustainability assessment of energy technologies and systems.

Background

The visibility of energy efficiency behavior allows the person who observes the behavior (observer) to be influenced by what they see while allowing the person being observed (actor) to signal their status or identity. However, other motives have not been studied in relation to the visibility of energy efficiency behavior, such as following norms. Furthermore, the relationship between the perceptions of observers and actors of energy efficiency behavior is unclear. The aim of this study is to establish a relationship between the perspectives of the observer and the actor, in relation to the visibility of energy efficiency measures. Specifically, the observer’s and actor’s perception of three motives was assessed: namely, the signaling status, signaling environmental identity, and adherence to norms. Other potential motives were also considered. Via semistructured interviews, participants were asked what they have observed regarding solar panels on other people’s houses and how they would feel about being seen (or not seen) were they to have solar panels and high-efficiency glass windows.

Results

The results show that people do not pay attention to whether a person is following norms but that they themselves would like to be seen as normal. When observing visible energy efficiency behavior, the observer tends to believe that the actor has high-level financial status and is intelligent, well educated, and an environmentalist. These characteristics have generally been perceived as being positive. However, people are against the idea that they may signal their environmental identity or status by making their energy efficiency measures visible. In contrast to the topic of signaling status and environmental identity, participants are comfortable discussing the esthetic appeal of energy efficiency measures, both as observers and as actors.

Conclusions

This study reveals differences in attitudes between observers and actors concerning signaling status and identity through visible energy efficiency behavior. An improved alignment or understanding of such attitudes could lead to people making their behaviors more visible. Norm-following and esthetics are found to affect behavior when it is visible, which implies that increasing the visibility of behavior by one person can lead to other people following suit. Finally, discussions on and improvements in the esthetic appeal of energy efficiency measures can foster interest and increase the adoption of such measures.

Background

The pursuit of sustainable development has become a global imperative, particularly for industrial and rich countries whose economies are heavily reliant on fossil fuels. Kuwait is a high-income nation reliant on fossil fuels and has one of the highest per capita CO₂ emissions reported globally. Kuwait has faced unique challenges in balancing economic growth with environmental sustainability. This study explores the nexus between economic growth, environmental quality, and sustainable development, emphasizing Kuwait’s efforts in decoupling its economic growth from ecological degradation. This paper integrates ecological indicators and social dimensions such as climate mitigation, renewable energy adoption, and progress toward sustainable development goals.

Results

A multivariate analytical framework was employed to assess the interconnections among eleven key indicators spanning economic growth, environmental quality, and sustainable development. Quantitative methods included correlation analysis and regression modeling using data from credible sources, such as the World Bank, OECD, SDG Index, World Development Indicators (WDI), and relevant journal articles and technical reports. In addition, qualitative assessments were conducted to evaluate the interconnectedness of economic growth, environmental quality, and sustainable development are evaluated qualitatively. Sensitivity analysis was used to ensure robustness and reliability of the findings.

This study examined the interplay between economic growth, environmental quality, and sustainable development in Kuwait. Over the past two decades, Kuwait experienced a nearly 380% increase in GDP and an 80% rise in total CO₂ emissions; yet, per capita emissions declined by 41%. This reduction is largely attributed to significant progress in clean energy (SDG 7) and climate action (SDG 13). The results highlight a successful decoupling of economic growth from emissions, suggesting that sustainable development is achievable through targeted policies and investing in green technologies.

Conclusions

The statistical and qualitative analyses demonstrated that Kuwait has effectively decoupled economic growth from CO₂ emissions through consistent actions and strategic efforts. This achievement highlights that environmentally sustainable economic development is attainable through a combination of targeted policy measures, technological innovation, and strong institutional commitment. Notable progress in Sustainable Development Goals, particularly in clean energy (SDG 7) and climate action (SDG 13), has contributed to improved environmental outcomes. These findings emphasize the importance of customized national strategies that align economic objectives with the Sustainable Development Goals.

Background

South Sudan is a landlocked country in East-Central Africa. It faces significant energy challenges, with only 7% of the population having access to electricity. Its dependence on biomass and diesel fuel has increased deforestation and air pollution, leading to environmental and health issues that endanger the environment and public health. The aim of this study is to assess the potential, suitability, and seasonal variation of renewable energy sources, with a focus on wind and solar power. The motivation for this research is South Sudan's urgent need to diversify its energy resources, reduce its reliance on fossil fuels, and address the energy demand while mitigating environmental degradation. This study includes a historical analysis of the daily wind and solar data collected over a period of 40 years (1974–2014) at four meteorological stations in South Sudan. The monthly wind speed and direction distributions, wind power density and monthly sunshine duration and solar radiation are computed. XLSTAT 2023, Tubular, Google Sheet, and the Angstrom–Prescott model are used to analyse and estimate the wind power density and solar radiation.

Results

The results show the variations in wind speed, power density, and solar radiation at the chosen locations. The wind-energy potential varies across the sites, with Malakal and Juba showing the highest annual average wind power densities—114.09 W/m² and 115.17 W/m², respectively—placing them in Class 3 (“Fair”). These are complemented by seasonal peaks of 373.31 W/m² (January, Malakal) and 220.16 W/m² (April, Juba), indicating strong suitability for medium-scale wind systems. Wau and Raga, with lower annual averages (88.32 and 58.07 W/m²), fall into Class 2 (“Marginal”) but may be able to support small-scale or hybrid solutions. Wind-direction patterns vary, requiring tailored micro-siting strategies. Solar resources exhibit greater consistency, with annual solar radiation averages between 19.56 and 19.72 MJ/m²/day across all sites, classifying them under “Moderate Solar Radiation.” Seasonal peaks of 22.22 MJ/m²/day in Wau (September) and 21.62 MJ/m²/day in Raga, place certain months in the “High Radiation” category, reinforcing the possibility for diverse solar technologies and hybrid systems.

Conclusions

This quantitative assessment offers clear perspectives into the renewable-energy landscape of South Sudan, emphasizing the potential of solar and wind energy to address the country’s energy crisis. The findings provide a foundation for policymakers and investors to strategically develop wind and solar projects aligned with the global sustainable development goals, particularly SDG 7 (affordable and clean energy), SDG 13 (climate action), and SDG 9 (industry, innovation, and infrastructure), which foster economic growth and reduce the dependency on fossil fuels.