Energy, Sustainability and Society最新文献

Background

In the energy transition framework towards decarbonization, green hydrogen, obtained through water electrolysis powered by renewable energy, is gaining importance. In order to pave the way to its production and trade, it is required to assess its main advantages and challenges, which are not only energy-related but involve also techno-economic, social, environmental, and geopolitical aspects. In line with this, the current article aims to provide a Multi-criteria Spatial Decision Support System to investigate the suitability of North African countries with respect to the production of solar-based hydrogen and its potential trade, and to apply it to Tunisia, as one of the most promising countries for becoming a competitive hydrogen exporting leader. Combining the Analytic Hierarchy Process and the Geographical Information System, this study focuses on evaluating the land suitability for solar hydrogen production at a country level, serving as the foundation for a methodology applicable across the entire North African region. After defining ten different criteria, these are spatially analysed and then prioritized according to different experts’ preferences, so that a final suitability map is obtained. The added value of the study is the inclusion of social and geopolitical criteria in this kind of assessments, often focused on techno-economic parameters alone.

Results

The suitability map allows to classify the majority of the Tunisian areas as moderately or highly suitable, even if the most favourable areas in terms of availability of resources are often negatively influenced by the geopolitical or economic assessment. The sensitivity analysis has also proved the high suitability of Tunisia, with no areas assessed as very low suitable even if the different criteria are extremized.

Conclusions

Among the several influencing factors addressing the suitability for green hydrogen uptake, this article makes it possible to explore the social and geopolitical externalities, as well as the environmental and techno-economic dimensions. Even if stakeholders’ preferences affect the final results, the sensitivity analysis makes it possible to test their robustness. Supporting the adoption of new clean technologies towards the carbon-neutrality target, the methodological framework could be applied for other countries and also tailored on other specific technological pathways.

Background

Globally and regionally, nations are going through a period of important changes determined by the climate and environmental challenges in the context of the transition towards green economy, by the energy crisis caused by the Russian–Ukrainian war started in 2022, as well as the economic and social effects of the COVID-19 pandemics, which all affect economic growth. Providing a sustainable development capable of contributing to the increase of welfare and life longevity requires high rates of economic growth as well as a healthy living environment. At present, boosting the transition to the green economy is considered as an alternative. Based on a multivariable linear regression model, this study aims to analyze the connection and influence of five macroeconomic indicators, on Romania’s economic growth over a period of 16 years (2006–2021), where the indicators are considered to be representative for green economy.

Results

The results pinpoint both the existence of a positive and long-term relation among the total greenhouse gas emissions, the value of the production of environmental goods and services, the total environmental taxes and real GDP, and the negative impact of the total generation of renewable electricity and investments for environmental protection upon real GDP. These results provide a relevant picture of the complex interdependences between the environmental indicators and economic growth, amid the significant challenges determined by the implementation of sustainability strategies.

Conclusions

Romania’s transition towards green economy not only represents an initiative based on the obligations resulting from joining the European Union’s Green Agenda, but also results from acknowledging the consequences of climate change; in accordance, our analysis intends to empower policy makers in intensifying the current levels of the total generation of renewable energy and the investments for environmental protection, so that these might reach the thresholds required to transform them into decisive factors of economic growth.

Background

The transition to renewable energy is crucial for decarbonising the energy system but creates land-use competition. Whilst there is consensus on the need for local responsibility in achieving climate neutrality, debates continue over where to implement renewable energy plants. The Public Participation Geographic Information System (PPGIS) scenario approach can facilitate these debates and improve equity and procedural and distributive justice.

Results

The findings highlight the effectiveness of the PPGIS method in assessing the spatial impact of technologies on agriculture and landscapes. The approach was tested in a rural German municipality to help stakeholders and citizens recognise the potential for land-based solar energy even under strict constraints. These insights were shared to support decision-makers on land-use changes to increase renewable energy production.

Conclusions

The findings indicate that the PPGIS scenario approach is valuable for improving equity and mutual understanding in local decision-making processes. Incorporating stakeholders’ and citizens’ perspectives into renewable energy planning enhances the transparency, legitimacy, and acceptability of land-use decisions. The ability to visualise and quantitatively assess different scenarios makes PPGIS particularly useful for addressing the complexities of public debates on land-use requirements for renewable energy systems.

Background

Achieving climate neutrality in cities is a major challenge, especially in light of rapid urbanization and the urgent need to combat climate change. This paper explores the role of advanced computational methods in the transition of cities to climate neutrality, with a focus on energy supply and transportation systems. Central to this are recent advances in artificial intelligence, particularly machine learning, which offer enhanced capabilities for analyzing and processing large, heterogeneous urban data. By integrating these computational tools, cities can develop and optimize complex models that enable real-time, data-driven decisions. Such strategies offer the potential to significantly reduce greenhouse gas emissions, improve energy efficiency in key infrastructures and strengthen the sustainability and resilience of cities. In addition, these approaches support predictive modeling and dynamic management of urban systems, enabling cities to address the multi-faceted challenges of climate change in a scalable and proactive way.

Main text

The methods, which go beyond traditional data processing, use state-of-the-art technologies such as deep learning and ensemble models to tackle the complexity of environmental parameters and resource management in urban systems. For example, recurrent neural networks have been trained to predict gas consumption in Ljubljana, enabling efficient allocation of energy resources up to 60 h in advance. Similarly, traffic flow predictions were made based on historical and weather-related data, providing insights for improved urban mobility. In the context of logistics and public transportation, computational optimization techniques have demonstrated their potential to reduce congestion, emissions and operating costs, underlining their central role in creating more sustainable and efficient urban environments.

Conclusions

The integration of cutting-edge technologies, advanced data analytics and real-time decision-making processes represents a transformative pathway to developing sustainable, climate-resilient urban environments. These advanced computational methods enable cities to optimize resource management, improve energy efficiency and significantly reduce greenhouse gas emissions, thus actively contributing to global climate and environmental protection.

Background

Addressing global climate challenges necessitates a shift toward sustainable energy systems, with public acceptance of energy technologies playing a vital role in their successful adoption. While extensive research has been conducted on this topic, the lack of a unified framework for integrating various data and approaches from existing studies remains a challenge. This inconsistency makes it difficult to compare findings across different contexts and impedes the development of a comprehensive understanding of the factors influencing acceptance. This review aims to address this challenge by systematically evaluating the statistical methods used in ten large-scale studies on public acceptance of energy technologies in Western Europe published between 2012 and 2023. This Work allows researchers to more effectively compare methodologies and results, offering a transparent and structured approach for analysis, thereby enhancing the overall methodological assessment.

Main text

The review of ten large-scale studies identified valuable insights and opportunities for improving the analysis of public acceptance of energy technologies. Traditional methods like regression analysis have provided a solid foundation, highlighting key factors such as perceived benefits, trust, and attitudes. However, the review also revealed potential for growth by integrating more advanced techniques like AI-supported analysis, sentiment analysis, and agent-based modelling. These newer approaches offer the ability to capture complex, non-linear relationships and provide predictive insights. The introduction of statistical pattern graphics significantly enhances the clarity and comparability of methodologies, helping researchers to better understand and improve their approaches, ultimately supporting more accurate and impactful studies.

Conclusions

The review emphasizes the need for a unified analytical framework that integrates diverse methods, including both traditional statistical techniques and emerging approaches such as machine learning and sentiment analysis, to enhance the comparability of studies on public acceptance of energy technologies. By consolidating these varied methodologies into a cohesive framework, researchers can generate more consistent, robust insights that account for the complexities of public attitudes across different contexts. This unified approach not only improves the generalizability of findings but also provides stronger empirical evidence to guide policymakers in crafting more informed, effective strategies for promoting sustainable energy transitions at both local and global levels.

Background

Citizen participation is integral to the governance of sustainability transformations. Long-term participatory processes undergo various phases of opening up and closing down various scopes of the participation—with significant consequences for the legitimacy and impact of the participation process.

Methods

To gain a better understanding of these processes, we address the question of how and why participation processes are opened up or narrowed down. Through document analysis and key-informant interviews, we evaluate a case of long-term citizen participation linked to the development of a sustainable neighborhood energy system in northwestern Germany.

Results

Results show that normative, substantive, and instrumental imperatives contribute to opening-up dynamics in participation processes. Closing-down dynamics were observed in the narrowing of thematic, spatial, temporal, and methodological scopes, as well as in the range of the actors involved. Reasons for closing down were financial and temporal restrictions, conflicting interests, the need for expert input in decision making about highly technological questions, the institutionalisation of participation, and stakeholder fatigue.

Conclusions

This study provides a new framework for analysing citizen participation while highlighting the complexities and interrelations associated with citizen participation within the context of technological and urban development.

Background

The greenhouse gas (GHG) mitigation quota is a unique instrument in Europe that redistributes money from high emission to low emission fuel markets while forcing fuel distributors to reduce the average emissions of their fuels. This paper presents the design of the German 2022 GHG quota, places it in the context of environmental policy instruments, and examines its impact on the affected fuel markets in relation to other environmental policy instruments. We aim to provide insights that can be applied in industry and policymaking, and to provide a basis for further research, to highlight GHG quota trading as an alternative to allowance trading and carbon taxes. Field research was conducted in the form of expert interviews. Furthermore, intermediaries and brokers were contacted via email and asked for transaction data. In addition, a qualitative literature review was conducted and publications of responsible authorities as well as relevant legal texts were used to gather information.

Results

We find that the GHG quota trading overlaps with the structures behind emission standards and emission trading schemes and, therefore, falls under the category of tradable performance standards. However, it also contains aspects of a subsidy and interacts directly or indirectly with several different markets.

Conclusions

While the GHG quota trading system shows potential as an environmental policy tool, its effectiveness is hindered by market complexities and external disruptions. Addressing these challenges through targeted research and policy adjustments could enhance its impact and alignment with broader climate goals.

Background

Research on portfolio optimization for energy generation often does so from a financial perspective. This study addressed a unique challenge: determining which companies, amidst a globalized electricity market, should be retained for climate risk preservation during specialization. Utilizing weather and generation data from 106 power plants across Argentina, we adapted integer-portfolio-optimization tools. Originally designed for financial index funds, these tools helped us construct a portfolio of power plants for a resilient energy mix.

Results

Our findings revealed optimal companies for retention by analyzing different portfolio configurations, where the number of plants was adjusted iteratively. In each iteration of the model, we selected a set of representative plants that minimize climate risk, which sometimes resulted in a plant being included in one portfolio but not another. This approach identified the specific companies and technologies essential for a diversified and climate-resilient energy portfolio while ensuring a strategic transition toward specialization and stabilizing generation risk in the face of variable weather conditions.

Conclusions

This paper presents a groundbreaking solution for specialization in a globalized energy market. Through portfolio optimization, we identified pivotal companies for each stage of the transition in Argentina. Firms like Parque Eólico La Genoveva and Complejo Hidroeléctrico Centrales Cacheuta Alvarez Condarco, showcased the balance needed for wind and hydroelectric sources. These insights should be used to guide policymakers to ensure a controlled and effective transition while maintaining stable generation risk.