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Despite decades of European social dialogue, little is known about the social policy preferences of EU employers' organizations (EEOs). Building on the literature on industrial relations and the role of business in welfare state development, this article explores the preferences of key EEOs (BusinessEurope, SGI Europe and SMEunited) in vocational education and training (VET), active and passive labour market policies, pensions and work–family reconciliation. Software-based qualitative content analysis of 75 position papers and 19 joint declarations, triangulated with four elite semi-structured interviews, is employed to assess employers' preferences along four national and two European dimensions. Largely in line with the power resources theory, EEOs favour cost containment and social investment, by strengthening labour market flexicurity and reducing skills mismatches through VET. Conflicting logics of membership and influence guide the actions of EEOs: members are wary of legislation impinging on national social policy traditions; yet, greater European assertiveness makes lobbying efforts unavoidable. [ABSTRACT FROM AUTHOR]

Climate change mitigation in the tourism sector is expanding research areas due to the importance of this sector and its rapid expansion. Aviation's contribution was found to be the most important source of GHG emissions from tourism. Also, the hospitality sector contributes a lot to GHG emissions in tourism destinations. Hospitality, constituting an essential component of the tourism industry, is a sector that has a high potential to reduce GHG and use of energy and water resources. Therefore, it is important to monitor the climate change mitigation performance of tourism destinations to achieve decarbonization of the tourism sector. The main objectives of this paper are to develop indicators of climate change mitigation performance of tourism destinations based on GHG indicators for the transport and hospitality sectors and apply this framework to assessment and ranking based on climate change mitigation performance of 4 main EU geographical regions as tourism destinations: Central, Northern, Southern, and Western Europe. This paper's main methodological approach is comparing and ranking different geographical regions in the European Union by assessing their climate change mitigation performance as tourist destinations. The study's main results showed that Finland, representing North Europe, is the best-performing country in climate change mitigation in tourism destinations. The second-best-performing geographical region is Western Europe. The worst-performing EU region based on climate change mitigation in tourism destinations was Central Europe. The South Europe region was found to be in a slightly better position than Central Europe but worse in comparison with Western Europe and especially in comparison to Northern Europe. The study's main implications provide policy recommendations for Central Europe as a tourism destination to increase energy and water use efficiency and the carbon footprint of the tourism sector. [ABSTRACT FROM AUTHOR]

DC railway electrification was deployed at the beginning of the 20th century in several countries in Europe. Today, this power system is no longer adapted to the demands of increased rail traffic. Due to the relatively low voltage level, the current consumed by the trains reaches several kAs. So, in the worst case, the locomotives cannot operate at their rated power due to the voltage drop along the contact line. Conventional solutions to reduce the voltage drop consist of increasing the cross-section of overhead lines or reducing the length of sectors by installing additional substations. Nevertheless, these solutions are expensive and not always feasible. The implementation of a Modular Battery Energy Storage System (MBESS) can be an alternative solution to reinforce the railway power supply. This paper first presents an MBESS based on elementary blocks associating Full-SiC Isolated DC-DC converter and battery racks. The electrical models of a railway sector and an elementary block are described, and simulations are performed considering real railroad traffic on two sectors of the French National Rail Network, electrified at 1.5 kV. The results show that the installation of an MBESS in the railway sector boosts the locomotive's voltage while also increasing overall system efficiency. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

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Hydropower has played an important role in Europe in recent decades, offering a unique combination of safe, low-cost, and clean power generation. Today, it is still one of the largest renewable energy sources (RES), accounting for about 35 % of RES electricity generation. However, grid stability is threatened by the increasing amount of undispatchable RES. Flexibility and dynamics such as energy storage and rapid response are urgently needed to achieve EU policy goals. In such a context, hydropower can play a key role, not only as a provider of regulated renewable energy but also due to its ability to balance a renewable energy system in the short term and the medium/long term by pumped storage technology. All these aspects underline the new role of hydropower, which aims to strengthen grid stability and power supply resilience and to enable higher penetration of volatile RES. In this context, the aim of this paper is to demonstrate the role of hydropower at the European level as well as the needs and opportunities of modernization to fully exploit its potential. In particular, this study provides, the assessment of the today flexibility offered by hydropower to the power system at the European level by leveraging a database of information collected through the participants to the working groups of the COST Action Pen@Hydropower which includes stakeholders in the hydropower sector of 34 European countries. The study confirms the key role of hydropower in future energy scenarios with 30 % of the flexibility demand at all time scales met by hydropower. Furthermore, the paper presents a review of the digitalization solutions and innovative technologies that support the growth of a new generation of sustainable hydropower together with the modernization opportunities for existing hydropower plants. The results of this work have practical implications for stakeholders in the hydropower sector and policymakers as it provides evidence at the European scale of the role of hydropower technology in balancing the power system and the need to have supportive frameworks and adequate markets to fully exploit the European hydropower potential to achieve the energy transition goals. [ABSTRACT FROM AUTHOR]

Europe assumed to become the first climate neutral continent but, due to the political situation on the eastern border, the security of the energy supply is threatened and it is possible that the existing coal installations will be used for more than originally expected. The paper exposes the ways in which the carbon dioxide resulting from the combustion of fossil fuels can be separated and thus the greenhouse gas emissions of the power plants can be close to zero. [ABSTRACT FROM AUTHOR]

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the imperative of leveraging renewable indigenous energy sources becomes evident. However, as such technologies are integrated into the existing system, it is necessary to shift from the current centralized infrastructure to a decentralized production strategy. This paper presents a method to identify a panel of technological solutions at the district level, intended to reduce complexity for the integration of decentralized models into a national-scale model. The framework's novelty lies in combining a global sensitivity analysis for solution generation with clustering to identify typical configurations. The global sensitivity analysis is performed on a mixed integer linear programming model, which optimally sizes and operates district energy systems. The sensitivity analysis determines the most influential parameters of the model using the Morris method and provides a representative sampling of the solution space by leveraging the Sobol sampling strategy. The latter is then clustered using a density-based algorithm to identify typical solutions. The framework is applied to a suburban and residential Swiss neighborhood. The first outcome of the research is the high sensitivity of the model to energy carrier prices. As a result, Sobol's sampling space separates itself into two system types: those based on a natural gas boiler and those relying on a combination of electrical heaters and heat pumps. For both types, the electricity demand is either fulfilled by PV panels or electricity imports. The identified configurations showcase that the framework successfully generates a panel of solutions composed of various system configurations and operations being representative of the overall solution space. • Energy carriers' retail prices are the most influential parameters of a district energy system. • Energy system configurations differ according to the consumed energy carrier and PV penetration. • PV deployment is affected by electricity tariffs but not by technological implementations. [ABSTRACT FROM AUTHOR]

• Thermal resilient building is able to absorb and adapt during the disruptive event and recover after it. • Passive and active energy efficiency measures can improve building thermal resilience. • Metrics of passive resilience estimate envelope capability in maintaining the desired indoor temperature. • Metrics of active resilience assess systems capacity in maintaining the desired minimum services. The energy performance of buildings is strongly influenced by several foreseeable and unforeseeable events during the operation phase, including natural hazards or energy supply disruptions. The increase of these events has raised attention on resilience, identified as the capability of buildings to react to such events guaranteeing the continuation of their operations. This paper investigates the resilience of residential reference buildings sited in different European climates (Athens, Berlin, Madrid, Turin, Stockholm). It simulates an eventual disruption of gas supply during winter carrying out energy dynamic simulations. Proposing new metrics to assess the building resilience, this paper quantifies the passive and active resilience of the main building components, both envelope and HVAC systems. It also analyses the effect of retrofit interventions on building resilience depending on climate. Results enlighten possible emergency interventions with backup electricity solutions to be activated to guarantee minimum services during the disruptive event associated with climate or geopolitical issues. The importance of increasing self-sufficiency in buildings through renewable energy sources is emphasized to favour the continuation of services in case of energy supply interruption. Finally, the link with energy security and independence is outlined together with possible strategies to increase the building stock resilience in Europe. [ABSTRACT FROM AUTHOR]

[Display omitted] • Market relevance of aboveground hydrogen storage is investigated. • Aboveground storage will likely play a much smaller role than underground storage. • Storage costs are the most decisive factor for market relevance. • Efficiency and systemic suitability can improve aboveground storage competitiveness. • Based on current projections, aboveground storage will only gain small market shares. Hydrogen storage is expected to play a crucial role in the comprehensive defossilization of energy systems. In this context, the focus is typically on underground hydrogen storage (e.g., in salt caverns). However, aboveground storage, which is independent of geological conditions and might offer other technical advantages, could provide systemic benefits and, thereby, gain shares in the hydrogen storage market. Against this background, this paper examines the market relevance of aboveground compared to underground hydrogen storage. Using the open-source energy system model and optimization framework of Europe, PyPSA-Eur, the influence of geological independence, storage cost relations, and technical storage characteristics (i.e., efficiencies) on mentioned market relevance of aboveground hydrogen storage are investigated. Further, the expectable market relevance based on current cost projections for the future is assessed. The studies show that in terms of hydrogen capacities, aboveground hydrogen storage plays a considerably smaller role compared to underground hydrogen storage. Even when assuming comparatively low aboveground storage cost, it will not exceed 1.7% (1.9 TWh H2,LHV) of total hydrogen storage capacities in a cost-optimal European energy system. Regarding the amounts of annually stored hydrogen, aboveground storage could play a larger role, reaching a maximum share of 32.5% (168 TWh H2,LHV a-1) of total stored hydrogen throughout Europe. However, these shares are only achievable for low cost storage in particularly suited energy system supply configurations. For higher aboveground storage costs or lower efficiencies, shares drop below 10% sharply. The analysis identifies some especially influential factors for achieving higher market relevance. Besides storage costs, the demand-orientation of a particular aboveground storage system (e.g., hydrogen storage at demand pressure levels) plays an essential role in market relevance. Further, overall efficiency can be a beneficial factor. Still, current projections of future techno-economic characteristics show that aboveground hydrogen storage is too expensive or too inefficient compared to underground storage. Therefore, to achieve notable market relevance, rather drastic cost reductions beyond current expectations would be needed for all assessed aboveground hydrogen storage technologies. [ABSTRACT FROM AUTHOR]

Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi‐year timescales. Increasingly, unsupervised machine learning techniques are used to identify representative weather patterns that can help simulate long‐term behavior. Here we develop a novel wind energy workflow that combines the weather patterns from unsupervised clustering with a numerical weather prediction model (WRF) to obtain efficient long‐term predictions of wind farm power and downstream wakes, which provide a good approximation to full WRF simulations at vastly reduced computational cost. We use ERA5 reanalysis data and compare clustering on low altitude pressure and wind velocity, a more relevant variable for wind resource assessment. We also compare varying domain sizes for the clustering. A WRF simulation is run at each cluster center and the results aggregated into a long‐term prediction using a novel post‐processing technique. We consider two case study regions and show that our long‐term predictions achieve good agreement with a year of WRF simulations in 2% of the computational time. Moreover, clustering over a Europe‐wide domain produces good agreement for predicting wind farm power output, but clustering over smaller domains is required for downstream wake predictions which agree with the year of WRF simulations. Our approach facilitates multi‐year predictions of power output and downstream farm wakes, by providing a fast, reliable, and flexible methodology applicable to any global region. Moreover, this constitutes the first tool to help mitigate effects of wind energy loss downstream of wind farms. Plain Language Summary: With the need to transition to renewable energy, wind energy is becoming increasingly important. However, the build‐out of wind farms must be carefully planned, not least because entire wind farms extracting energy from the atmosphere cause large scale wind velocity deficits downstream. Wind energy resource assessment typically relies on numerical models, but these are computationally expensive to run for the multi‐year predictions required for sustainable development. It is therefore common to use a smaller set of weather conditions that are representative of a longer period. Here, we develop such a workflow based on identifying characteristic weather patterns using a clustering technique from the machine learning literature. Based on the characteristic patterns identified, we perform a small number of short numerical model experiments, one for each weather pattern. By applying a novel post‐processing technique to combine the outputs from these experiments, we obtain predictions of wind farm power and downstream wakes over the original multi‐year period, using just 2% of the computation time required by a purely numerical modeling approach. Our workflow thus constitutes a fast, reliable, and flexible methodology that is applicable to any global region, which can be used for the sustainable development of wind energy. Key Points: Fast, reliable and flexible method for long‐term wind energy resource assessment using only six short‐term simulationsEvaluation of suitable variables and domain sizes for deriving weather patternsWeather prediction model is combined with clustering method for energy prediction [ABSTRACT FROM AUTHOR]

In the twentieth century national social policies stabilized the European state systems, favouring domestic concordance and citizens' support to the nation-building process. Welfare institutions have historically served this key political function also in federal systems, where social citizenship has been used as a tool to foster unity. In contrast, even though the EU devotes a consistent part of its (however limited) budget to social cohesion and inclusion programmes, it takes little credit for such efforts. Building on original survey data on public opinion collected in 2019 across ten EU countries, this article shows that, indeed, only a limited number of citizens are aware of the social role played by the EU in their local community. On the other hand, it demonstrates that citizens' awareness of EU programmes strengthens the individual perception of power resources stemming from euro-social initiatives, the feeling of 'being heard' by the EU and, ultimately, the support for the European integration project as a whole. By implication, increasing the relevance and visibility of euro-social programmes could possibly reinforce the very foundations of the EU. [ABSTRACT FROM AUTHOR]

To achieve the European climate targets and the Paris Agreements, at least 65% of the electricity needs to be generated from renewable energy sources by 2030. This requires a significant increase of distributed energy resources, posing a challenge for distribution system operators to integrate them into existing hierarchical grids. The concept of Local Flexibility Markets has recently gained attention as a market-based tool to tackle this challenge, making use of demand side flexibility. In this paper a Delphi method has been performed, showing that there are still numerous barriers in place preventing a widespread adoption of such markets in Europe. The main obstacles for market participants refer to standardisation issues. Based on that, a hybrid market model has been developed, comprising elements of a Local Flexibility Market and a Local Energy Market. To activate demand side flexibility from local energy transactions, spatio-temporally varying price signals are introduced, reflecting the constraints of the distribution grid. The paper shows, that this novel market approach helps to overcome relevant standardisation issues, but also certain barriers regarding end-users' lifestyles, which is because prices are comprehensible signals that can motivate end-users to participate. Moreover, a set of numerical examples is provided to illustrate the monetary benefits that could be gained by consumers and prosumers in the proposed hybrid market model. The examples show that the major share of the cost savings result from local energy trading, but the hybrid market model is also able to accumulate additional smaller revenues from providing flexibility. Finally, the systematic approach of characterising the market model in this paper offers a valuable framework for other researchers to map their ideas among existing approaches of Local Energy and Flexibility Markets. • A framework for characterising Local Energy and Flexibility Markets is provided. • Main barriers for adopting such markets are related to standardisation issues. • A hybrid market model is proposed, featuring local energy and flexibility trading. • Trades are steered by dynamic price signals, reflecting various flexibility needs. • Numerical examples indicate the expected financial implications. [ABSTRACT FROM AUTHOR]

Collective bargaining institutions are correlated with better population health. However, there are still major gaps in our understanding regarding the impact of collective bargaining on health inequalities, particularly between labour market 'insiders' and 'outsiders'. In this study, we investigate the effect of collective bargaining coverage on individuals' self-rated health, and whether the impact varies according to labour market status. We use four waves of the European Values Survey (1981–2018) and three-level nested random intercept models across 33 OECD and European countries (N  = 66 301). We find that stronger and more inclusive collective bargaining institutions reduce health inequalities between the unemployed and the employed by disproportionately improving the health of the unemployed. This study implies that targeting the political institutions that shape the distribution of power and resources is important for reducing health inequalities. [ABSTRACT FROM AUTHOR]

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination ( R 2 ) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515. [ABSTRACT FROM AUTHOR]

The European countries are committed to making Europe the first carbon‐neutral continent by 2050 under the carbon leakage debates. Carbon leakage occurs when carbon‐intensive production relocates to environmentally unregulated countries. The trade channel of carbon leakage refers that the carbon‐loaded products finally come back through imports and increase consumption‐based carbon emissions in the decarbonization‐committed countries. This study probes the effects of extra‐imports (imports from non‐European countries) share on per capita consumption‐based carbon dioxide emissions (CCEpc) in the panel of 31 European countries from 1995 to 2018. After identifying cross‐country dependence, unit root, heterogeneity, and cointegration, the study applies the common correlated effects mean group (CCEMG) and augmented mean group (AMG) estimators, followed by the Emirmahmutoglu‐Kose causality test. The results reveal a carbon leakage pattern that extra‐imports share has both associative (positive) and causal (one‐way) effects on CCEpc. Other results unveil a strong decarbonization contribution from enhancing renewable energy supply against the carbonization forces of growing extra‐exports (exports to non‐European countries) share, real gross domestic product, and comparative advantage in high‐tech manufactures, while population density's influence is statistically insignificant. Some policy implications including carbon‐adjusting border taxes on trade are drawn for regional and global mitigation undertakings. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the relationship between the fuel quality and the gaseous and particulate pollutant emissions generated from a hot-water boiler during the combustion of different types of innovative processed fuels: smokeless coal, smokeless briquettes 1–3, smokeless pellets and unprocessed hard coal. The aim of our research was to prove the presumption that smokeless processed coals produce less gaseous and particulate emissions. By using modern fuels in already used and manufactured older boilers, there is a possibility to significantly reduce emissions of organic gaseous compounds (OGC) and polycyclic aromatic hydrocarbons (PAHs). The emission situation in the heating season can be significantly improved even without costly production, and thus consumption of natural resources and energy, and installation of modern boilers. Physical and chemical characterization of solid-fuel samples, including determination of moisture content, ash, volatile organic content, calorific value and elemental composition analysis, were performed. Fuels were burned in one type of hot-water boiler class 1 according to EN 303-5 to determine the impact of applied fuel types on pollutant emissions. The pollutant emissions were characterized by the contents of gaseous components: nitrogen oxides NOx, sulfur dioxide SO2, carbon monoxide CO, carbon dioxide CO2, organic gaseous compounds OGC and particle components: total suspended particles TSP, particulate matter less than 2.5 µm and 10 µm (PM2.5 and PM10, respectively) and polycyclic aromatic hydrocarbons PAHs in both phases. The emission factors from six types of fuel were compared with applicable European standards. The lowest NOx content was observed for smokeless briquette 1, while the lowest SO2 content was observed for smokeless pellets. The emission of CO was at a similarly low level of 200 g/kg for smokeless briquette 1, smokeless briquette 2 and hard coal. Gaseous and pollutant emissions described by PM2.5 and TSP were observed to be the lowest for smokeless coal, smokeless briquette 1 and smokeless briquette 2. [ABSTRACT FROM AUTHOR]

Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter's ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage ( T H D u ), and current ( T H D i ). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids. [ABSTRACT FROM AUTHOR]

Global climate change brings environmental quality sensitivity, especially in European countries. In this context, European countries are striving to achieve carbon neutrality by 2050. Renewable energy use can be an effective way to curtail the increasing environmental degradation. How the oil prices shape the energy use pattern and environmental quality remains unclear to date. Therefore, the present research examines the linkage between energy use (non-renewable and renewable energy), oil prices, and CO2 emissions in 30 European countries between 1997 and 2017. The study applied fully modified ordinary least squares (FMOLS), Westerlund (2007) cointegration and the Driscoll-Kraay Regression tests to explore these associations. The result of individual samples and the full sample provides the same intimations for the use of energy in Europe. The outcomes of the research ratify the existence of a long-run relationship among variables and found that non-renewable energy consumption increases CO2 emissions, while renewable energy consumption mitigates CO2 emissions. Furthermore, when the oil prices rise, people switch towards substitute energy sources that reduce CO2 emissions. Based on the results, the study suggests that European countries should increase the share of renewable energy, promote the use of clean energy resources, and discourage fossil fuel energy consumption to ensure sustainability. [ABSTRACT FROM AUTHOR]

Water scarcity is a phenomenon that is occurring more and more frequently in larger areas of Europe. As a result of drought, there are significant drops in yields. As demand for food continues to rise, it is becoming necessary to bring about a substantial increase in crop production. The best solution to water scarcity appears to be irrigation for crops that are particularly sensitive to drought. Today, many technical solutions are used to supply and distribute water to crops. The optimal solution is drip irrigation, which makes it possible to deliver water directly to the plant root system to save melting freshwater resources. In the article special attention was paid to methods of supplying electricity to power irrigation pumps. The analysis was made for areas with a significant distance between the agricultural land and the urbanised area (which has water and electricity). The authors have selected the parameters of an off-grid photovoltaic mini-hydropower plant with energy storage (with a power of 1.36 kW). An analysis was made of the profitability of such an investment and a comparison with other types of power supply. Based on the performed calculations, a prototype power supply system equipped with photovoltaic panels was made to show the real performance of the proposed system. The tests carried out showed that the irrigation pump will be powered most of the time with a voltage whose parameters will be very close to the nominal ones. [ABSTRACT FROM AUTHOR]

The increasing concern on the world waste issue has speed up the adoption of the circular economy model to replace the conventional “take, make, use, dispose” linear economy model. Life Cycle Assessment (LCA) has been widely used in the past to identify and evaluate the inputs and outputs of different activities carried out in a linear economy model. With more and more industry transition from linear economy model to Circular Economy (CE) model, especially in developed nations particularly Europe, United States and China, it is imperative for the LCA to consider the circularity relationship of resources in its framework to enhance the effectiveness of LCA. One of the fundamental principles of circular economy is that material waste is avoided by prioritizing the end-of-use capture of materials to create a positive value-driven closed loop. Nonetheless, the literature that include or integrate the circularity elements into the LCA framework remains limited. Anecdotal evidence has suggested that it is due to some limitations and barriers, including but not limited to industry acceptance level, high transition cost, lack of know-how expertise to enable the evaluation and others. In this work, the overview of the research and development towards the application of LCA to support CE strategies are reviewed. The limitations and challenges to apply LCA for a circular economy model are also identified to propose recommendations to extend the current application of LCA for a more robust strategy that capture the value of the reuse, recycle, remanufactured, repurposed resources and the potential upstream and downstream impacts for better decisions for sustainability. A case study in the palm oil industry is also presented to illustrate the possible development of CE-LCA framework in the context of developing countries. [ABSTRACT FROM AUTHOR]