ABSTRACT
Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the "Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models' outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.
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The increasing number of electric vehicles is forcing new solutions in the field of charging infrastructure. One such solution is photovoltaic carports, which have a double task. Firstly, they enable the generation of electricity to charge vehicles, and secondly, they protect the vehicle against the excessive heating of its interior. This article presents the functioning of a small carport for charging an electric vehicle. Attention is drawn to the problems of selecting the peak power of the photovoltaic system for charging an electric vehicle. An economic and energy analysis is carried out for the effective use of photovoltaic carports. In this article, we present the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of probability distribution.
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In the wake of the COVID-19 pandemic and the Russian invasion of Ukraine, many countries see coal as the easiest solution to their energy sector challenges, despite the consequences for climate goals. Several countries of the European Union started to re-evaluate their coal policies vis-à-vis the current energy crisis and, although such a change is expected to be short-term in nature, it nevertheless has negative consequences for the Union's 2050 climate goal. However, most of the EU countries did not revise their phase-out goals. This paper examines Slovakia as a country that embarked on a coal phase-out trajectory only a few years before the pandemic broke out and stayed firmly on this path despite benefits stemming from the continued use of domestic coal. Domestic coal used to be considered a safeguard of energy security in Slovakia, especially after the 2009 gas crisis. However, a decision was made in 2018 to phase out coal by 2023, and this has not changed despite increased focus on domestic energy sources as energy security guarantors during the current energy crisis. This paper explains the decision in favour of a coal phase-out and its support vis-à-vis the energy crisis using the concept of ‘financial Europeanisation', which stresses the importance of EU funds for the development of the domestic policies of EU member states. While the expected funds serve as a catalyst for the coal phase-out needed to reach climate goals, short-term advantages of revising a coal phase-out were outweighed by long-term benefits provided by EU funds.
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Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.
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PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
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The increasing concentration of anthropogenic CO2 in the atmosphere is causing a global environmental crisis, forcing significant reductions in emissions. Among the existing CO2 capture technologies, microalgae-guided sequestration is seen as one of the more promising and sustainable solutions. The present review article compares CO2 emissions in the EU with other global economies, and outlines EU's climate policy together with current and proposed EU climate regulations. Furthermore, it summarizes the current state of knowledge on controlled microalgal cultures, indicates the importance of CO2 phycoremediation methods, and assesses the importance of microalgae-based systems for long-term storage and utilization of CO2. It also outlines how far microalgae technologies within the EU have developed on the quantitative and technological levels, together with prospects for future development. The literature overview has shown that large-scale take-up of technological solutions for the production and use of microalgal biomass is hampered by economic, technological, and legal barriers. Unsuitable climate conditions are an additional impediment, forcing operators to implement technologies that maintain appropriate temperature and lighting conditions in photobioreactors, considerably driving up the associated investment and operational costs.
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Having a prediction model for air quality at a low computational cost can be useful for research, forecasting, regulatory, and monitoring applications. This is of particular importance for Latin America, where rapid urbanization has imposed increasing stress on the air quality of almost all cities. In recent years, machine learning techniques have been increasingly accepted as a useful tool for air quality forecasting. Out of these, random forest has proven to be an approach that is both well-performing and computationally efficient while still providing key components reflecting the nonlinear relationships among emissions, chemical reactions, and meteorological effects. In this work, we employed the random forest methodology to build and test a forecasting model for the city of Buenos Aires. We used this model to study the deep decline in most pollutants during the lockdown imposed by the COVID-19 (COronaVIrus Disease 2019) pandemic by analyzing the effects of the change in emissions, while taking into account the changes in the meteorology, using two different approaches. First, we built random forest models trained with the data from before the beginning of the lockdown periods. We used the data to make predictions of the business-as-usual scenario during the lockdown periods and estimated the changes in concentrations by comparing the model results with the observations. This allowed us to assess the combined effects of the particular weather conditions and the reduction in emissions during the period when restrictions were in place. Second, we used random forest with meteorological normalization to compare the observational data from the lockdown periods with the data from the same dates in 2019, thus decoupling the effects of the meteorology from short-term emission changes. This allowed us to analyze the general effect that restrictions similar to those imposed during the pandemic could have on pollutant concentrations, and this information could be useful to design mitigation strategies.The results during testing showed that the model captured the observed hourly variations and the diurnal cycles of these pollutants with a normalized mean bias of less than 6 % and Pearson correlation coefficients of the diurnal variations between 0.64 and 0.91 for all the pollutants considered. Based on the random forest results, we estimated that the lockdown implied relative changes in concentration of up to -45% for CO, -75% for NO, -46% for NO2, -12% for SO2, and -33% for PM10 during the strictest mobility restrictions. O3 had a positive relative change in concentration (up to an 80 %) that is consistent with the response in a volatile-organic-compound-limited chemical regime to the decline in NOx emissions. The relative changes estimated using the meteorological normalization technique show mostly smaller changes than those obtained by the random forest predictive model. The relative changes were up to -26% for CO, up to -47% for NO, -36% for NO2, -20% for PM10, and up to 27 % for O3. SO2 is the only species that had a larger relative change when the meteorology was normalized (up to 20 %). This points out the need for accounting not only for differences in emissions but also in meteorological variables in order to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. We believe that the model itself can also be a valuable contribution to a forecasting system in the city and that the general methodology could also be easily applied to other Latin American cities as well. We also provide the first O3 and SO2 observational dataset in more that a decade for a residential area in Buenos Aires, and it is openly available at 10.17632/h9y4hb8sf8.1 .
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A unified framework that connects emissions with satellite‐observed column amounts is derived from first principles. The emission information originates from the inner product of the horizontal wind and the gradient of column amount, which is more accurate than the horizontal flux divergence as used in previous studies. Additionally, the topographical and chemical effects are accounted for through fitted scale height and chemical lifetime. This framework is applied to derive NOx and CO emissions over the CONUS from TROPOspheric Monitoring Instrument NO2 and CO observations. High‐resolution (0.04°) emission mapping over the CONUS reveals unprecedented details, including CO emissions in major cities and NOx emissions from large cities, power plants, and major roadways. Monthly resolved NOx emissions show decrease and rebound after the COVID‐19 pandemic. This framework is integrated with the physical oversampling algorithm and can be readily applied to other products from the new‐generation satellite instruments.Alternate :Plain Language SummarySatellites usually measure the vertically integrated column amount of atmospheric species from space. For short‐lived species like nitrogen oxides, the observed column amount indicates location and strength of emission sources. However, atmospheric dispersion smears the relationship between emission and column amount as the lifetime of species gets longer. This study directly maps emission based on the principle of mass balance. Namely, the spatial gradient of column amount should align with horizontal wind if there is an emission. Additionally, topography and chemical reaction may cause spatial gradients of column amount that are unrelated to emissions and are accounted for. Unprecedented details in the emission of air pollutants are unveiled by applying this approach to the TROPOspheric Monitoring Instrument products.
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This study investigated the spatiotemporal variabilities in nitrogen dioxide (NO2), formaldehyde (HCHO), ozone (O3), and light-absorbing aerosols within the Greater Tokyo Area, Japan, which is the most populous metropolitan area in the world. The analysis is based on total tropospheric column, partial tropospheric column (within the boundary layer), and in situ observations retrieved from multiple platforms as well as additional information obtained from reanalysis and box model simulations. This study mainly covers the 2013–2020 period, focusing on 2020 when air quality was influenced by the coronavirus 2019 (COVID-19) pandemic. Although total and partial tropospheric NO2 columns were reduced by an average of about 10 % in 2020, reductions exceeding 40 % occurred in some areas during the pandemic state of emergency. Light-absorbing aerosol levels within the boundary layer were also reduced for most of 2020, while smaller fluctuations in HCHO and O3 were observed. The significantly enhanced degree of weekly cycling of NO2, HCHO, and light-absorbing aerosol found in urban areas during 2020 suggests that, in contrast to other countries, mobility in Japan also dropped on weekends. We conclude that, despite the lack of strict mobility restrictions in Japan, widespread adherence to recommendations designed to limit the COVID-19 spread resulted in unique air quality improvements.
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The devastating social, economic and mental disruption caused due to pandemic has forced the decisionmakers to rewrite the script and opens up a way for transformative resilience, green and more digitally enabled strategies and recovery leading to a next wave of economic prosperity. Prior to the pandemic, many recognized the need for a new economic model that is less environmentally damaging, not much dependent on the globalized linear supply chain, cheap raw material and is less wasteful. Post pandemic crisis calls for a need to transition to a new thinking, resilience, and sustainable and circular way of doing business in alignment with other global challenges. In this reference, this study provides an insight into what resilient circular economy strategies looks like post Covid-19. Also, the study highlights the Challenges and opportunities created by resilient circular economy (CE) towards a sustainable business model. For this purpose, a semi- structured interviews are conducted with 23 executives across industries on resilience and CE. The study concludes that the transition from old linear model to a new closed loop model is not as easy as it looks like, it requires a thoughtful collaboration, creating synergies between the systems, participation, resilience mindset (which involves rethinking, redefining, and reinventing our priorities, resources, skills), political momentum, connectivity, diversity and most importantly systems thinking. Study is conceptual and qualitative in nature. Analysis of interviews together with the literature forms the basis of the research study. The study suggest that the circular economy must embed a strong sociological basis to manage both slow social variables such as company culture, employee’s mindset, human capital, worker habit and feedbacks.
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A knowledge gap exists concerning how chemical composition and sources respond to implemented policy control measures for aerosols, particularly in a semi-arid region. To address this, a single year's offline measurement was conducted in Hohhot, a semi-arid city in northern China, to reveal the driving factors of severe air pollution in a semi-arid region and assess the impact of the COVID-19 lockdown measures on chemical characteristics and sources of PM2.5. Organic matter, mineral dust, sulfate and nitrate accounted for 31.5 %, 14.2 %, 13.4 % and 12.3 % of the total PM2.5 mass, respectively. Coal combustion, vehicular emission, crustal source and secondary inorganic aerosols were the main sources of PM2.5 in Hohhot, at 38.3 %, 35.0 %, 13.5 %, and 11.4 %, respectively. Due to the coupling effect of emission reduction and improved atmospheric conditions, the concentration of secondary inorganic components, organic matter and elemental carbon declined substantially from the pre-lockdown (pre-LD) period to the lockdown (LD) and post-lockdown (post-LD) periods. The source contribution of secondary inorganic aerosols increased (from 21.1 % to 37.8 %), whereas the contribution of vehicular emission reduced (from 35.5 % to 4.4 %) due to lockdown measures. The rapid generation of secondary inorganic components caused by unfavorable meteorological conditions during lockdown led to serious pollution. This study elucidates the complex relationship between air quality and environmental policy.
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The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightningNOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishingNO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.
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Decarbonization of the aviation sector is crucial to reaching the global climate targets. We quantified the environmental impacts of Power-to-Liquid kerosene produced via Fischer-Tropsch Synthesis from electricity and carbon dioxide from air as one broadly discussed alternative liquid jet fuel. We applied a life-cycle assessment considering a well-to-wake boundary for five impact categories including climate change and two inventory indicators. Three different electricity production mixes and four different kerosene production pathways in Germany were analyzed, including two Direct Air Capture technologies, and compared to fossil jet fuel. The environmental impacts of Power-to-Liquid kerosene varied significantly across the production pathways. E.g., when electricity from wind power was used, the reduction in CO2-eq. compared to fossil jet fuel varied between 27.6–46.2% (with non-CO2 effects) and between 52.6–88.9% (without non-CO2 effects). The reduction potential regarding CO2-eq. of the layout using low-temperature electrolysis and high-temperature Direct Air Capture was lower compared to the high-temperature electrolysis and low-temperature Direct Air Capture. Overall, the layout causing the lowest environmental impacts uses high-temperature electrolysis, low-temperature Direct Air Capture and electricity from wind power. This paper showed that PtL-kerosene produced with renewable energy could play an important role in decarbonizing the aviation sector.
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For many decades, the Region of Western Macedonia has been Greece’s energy hub, contributing significantly to electricity supply and national growth with the exploitation of lignite deposits for power generation. Lignite, though, has been banned from EU energy source policies towards achieving CO2 emissions reduction, with profound implications on the economy of the region. Despite the importance of this energy transition, a combinatorial analysis for the area in the coal phase-out regime is missing. Therefore, a combined analysis is performed here, and more specifically, a strengths, weaknesses, opportunities, and threats (SWOT) analysis in all the affected sectors, in combination with the examination of the degree of satisfaction with the EU’s energy priorities. The results of the study show that the Region of Western Macedonia has profound strengths and offers many new opportunities during its transition to a new production model. On the other hand, it has high unemployment rates and low rates of competitiveness and innovation. The main threat is the Region’s desertification due to the inability to find sufficient jobs. Considering the Energy Union’s priorities, the Region of Western Macedonia satisfactorily follows the priorities of Europe in its transition to the new production model, with plenty of room for improvement. The analysis performed allows for a just transition strategic planning to minimize social, economic and energy challenges while maximizing sustainable power generation and has implications for all relevant stakeholders, contributing to the implementation of Energy Union governance and climate actions.
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This paper addresses the energy efficiency issue in household appliances, which has led to the establishment of policies at a global level in favor of setting minimum energy performance standards (MEPS), which guarantee end users are able to select more efficient equipment. The countries of the United States, Brazil, Mexico, Chile, and the Community of the European Union were taken as references to review their policies and implementation strategies, in order to be compared with the Colombian panorama (at the market, technical and political levels). This allows the establishment of common aspects and differences related to the determination of energy consumption, adjusted volume, and formalization of efficiency ranges, and in the specific case of domestic refrigeration. Managing to distinguish the most relevant aspects for the successful adoption of these policies in Colombia. It is evident that the implementation of these guidelines has a positive impact on the market of the countries and communities of reference. Similarly, the MEPS are shown as a mechanism to regulate energy consumption in the residential sector.
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Brick kilns add enormous quantities of organic pollutants to the air that can cause serious health issues, especially in developing countries;poor air quality is associated with community health problems, yet receives no attention in Northern Pakistan. The present study, therefore, assessed the chemical composition and investigated the impacts of air pollution from brick kilns on public health. A field-based investigation of air pollutants, i.e., PM1, PM2.5 and PM10, CO2, CO, NO, NO2, H2S, and NH3 using mobile scientific instruments was conducted in selected study area locations. Social surveys were conducted to investigate the impacts of air pollution on community health. The results reveal the highest concentrations of PM1, PM2.5, and PM10, i.e., 3377, 2305, and 3567.67 µg/m3, respectively, in specific locations. Particulate matter concentrations in sampling points exceeded the permissible limits of the Pakistan National Environmental Quality Standard and, therefore, may risk the local population’s health. The highest mean value of CO2 was 529 mg/L, and other parameters, such as CO, NO, NO2, H2S, and NH3 were within the normal range. The social survey’s findings reveal that particulate matter was directly associated with respiratory diseases such as asthma, which was reported in all age groups selected for sampling. The study concluded by implementing air pollution reduction measures in brick kiln industries to protect the environment and community health. In addition, the region’s environmental protection agency needs to play an active role in proper checking and integrated management to improve air quality and protect the community from air hazards.
ABSTRACT
Climate change is taking place on a global scale and it is substantially affected by human activity, including increasing greenhouse gas emissions. One of the thematic objectives of EU’s new financial objective is a more environmentally friendly low-emission Europe that promotes clean and fair energy transformation, green investments, and a circular economy, among others. The Polish economy is mainly based on energy production from conventional sources (fossil fuels). Considering that the demand for electricity in Poland is predicted to increase by as much as 50% until 2040, it is necessary to take action aimed at increasing the share of renewable energy sources. The subject of analysis is the Opolskie Voivodeship (a NUTS 2 type region), the capital of which features the biggest Polish coal power plant. In 2014–2019, it was expanded by two units with 1800 MW in total capacity, thereby indicating that investments in energy obtained from conventional sources are still implemented and to a large extent at that (the expansion has been the biggest infrastructural investment in Poland since 1989). The Opolskie region is characterised by substantial excess in acceptable environmental burden (dust pollution, among others). The aim of the paper is to evaluate the key environmental conditions for the Opolskie region’s development in terms of the assumptions of the domestic and EU energy policies. The Opolskie region’s developmental challenges in the environmental area were determined on the basis of selected indicator estimations up to 2030. The research hypothesis assumes that the environmental conditions for the Opolskie region’s development are unfavourable. The methodological part features an analysis of the cause and effect dependencies in the “environment” area, which enabled an assessment of the Opolskie Voivodeship’s current situation as well as an analysis of the dependencies relevant to the region’s development. This was followed by an estimation of selected indicators in the “environment” area until 2030, which allowed for an assessment of their probable levels and thereby a specification of the region’s development conditions. The estimation was conducted using the data available in public statistics, i.e., Statistics Poland’s data. The indicators estimated for 2030 were presented using three forecasting methods: (a) the monotonic trend, (b) the yearly average change rate, and (c) the logarithmic trend.
ABSTRACT
Carbon dioxide (CO2) has reached a higher level of emissions in the last decades, and as it is widely known, CO2 is responsible for numerous environmental problems, such as climate change. Thus, there is a great need for the application of CO2 capture and storage, as well as of CO2 utilization technologies (CCUS). This review article focuses on summarizing the current CCUS state-of-the-art methods used in Europe. Special emphasis has been given to mineralization methods/technologies, especially in basalts and sandstones, which are considered to be suitable for CO2 mineralization. Furthermore, a questionnaire survey was also carried out in order to investigate how informed about CO2 issues European citizens are, as well as whether their background is relative to their positive or negative opinion about the establishment of CCUS technologies in their countries. In addition, social acceptance by the community requires contact with citizens and stakeholders, as well as ensuring mutual trust through open communication and the opportunity to participate as early as possible in the development of actions and projects related to CO2 capture and storage, at all appropriate levels of government internationally, as citizens need to understand the benefits from such new technologies, from the local to the international level.
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This paper demonstrates the need and potential for using waste heat recovery (WHR) systems from infrared gas radiant heaters, which are typical heat sources in large halls, due to the increasing energy-saving requirements for buildings in the EU and the powerful and wide-spread development of the e-commerce market. The types of gas radiant heaters are discussed and the classification of WHR systems from these devices is performed. The article also presents for the first time our innovative solution, not yet available on the market, for the recovery of heat from the exhaust gases of ceramic infrared heaters. The energy analysis for an industrial hall shows that this solution allows for environmental benefits at different levels, depending on the gas infrared heater efficiency, by reducing the amount of fuel and emissions for domestic hot water (DHW) preparation (36.8%, 15.4% and 5.4%, respectively, in the case of low-, standard- and high-efficiency infrared heaters). These reductions, considering both DHW preparation and hall heating, are 16.1%, 7.6% and 3.0%, respectively. The key conclusion is that the innovative solution can spectacularly improve the environmental effect and achieve the highest level of fuel savings in existing buildings that are heated with radiant heaters with the lowest radiant efficiency.
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Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.