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Current management of solid waste from pulp and paper activities represents an environmental and economic burden worldwide due to pollution emissions. This study investigates the potential of hydrothermal carbonisation (HTC) treatment as a sustainable alternative for producing cleaner and energy-dense solid fuel from paper mill sludge. The effect of process parameters (temperature, reaction time and solid load) on hydrochar fuel formation from paper sludge was evaluated and, for the first time, the paper sludge-derived hydrochar was optimised to maximise the mass yield and calorific value using response surface methodology (RSM). The physicochemical characteristics, thermal fuel behaviour, energy recycling efficiency and electricity generation potential were assessed by proximate and ultimate analysis, thermogravimetry, bomb calorimeter, scanning electron microscopy and process energy assessment. Results showed that hydrochar fuel formation and properties were mainly influenced by the process temperature and residence time, and governed by dehydration and decarboxylation reactions which reduced the atomic H/C and O/C ratios by 35.5% and 64%, respectively. The produced hydrochars presented low sulphur, nitrogen and ash content with a maximum calorific value (HHV) of 22.9 MJ/kg, equivalent to the HHV of coal for commercial utility in South Africa. The HHV of the hydrochar corresponded to a 49.80% increase over the HHV of the initial feedstock. The optimum operating conditions were 231 ± 1 °C and 1.99 h for a hydrochar yield of 74.4% and calorific value of 18.5 MJ/kg. The energy assessment showed that up to 58.34% of the energy produced by hydrochar fuel combustion may be recycled as heat or power, while the remaining 41.66% of the combustion energy could be utilised to sustain the HTC treatment of paper sludge. The substantial water demand was concluded to be a drawback. Thus, water recirculation and the potential to catalyse the HTC reactions to increase overall process efficiency will constitute a future study to make the process more environmentally friendly for industrial-scale application. [Display omitted] • Dehydration and decarboxylation were predominant. • Optimum energy yield of 18.5 MJ/kg and 74.4% hydrochar was achieved under 231 ± 1 °C and 1.99 h. • Higher HTC reaction severity enhanced the physicochemical properties of the hydrochar. • Hydrochars produced have low nitrogen and sulphur (<1%) content. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of enterprises is one of the key means to solve the problem of energy shortage. It is of great significance to investigate how environmental information disclosure (EDI) promotes the green total factor energy efficiency (GTFEE) of enterprises. Based on this, this study calculates the GTFEE of enterprises by combining the database of Chinese manufacturing and the pollutant emission of industrial enterprises and investigates the impact of EDI on the GTFEE of manufacturing industries by using a difference-in-difference model. The following is found: (1) EDI can significantly promote the manufacturing enterprises' GTFEE, and the results are still valid after a series of robustness tests; (2) Mechanism analysis shows that EDI can improve the GTFEE of manufacturing enterprises by promoting technological innovation and optimizing energy structure; (3) The heterogeneity analysis shows that EID is more positive on firms' GTFEE in the eastern than western regions. The positive impact is greater for non-state-owned, low-energy consumption, export, and polluting enterprises. The findings of this paper provide a theoretical basis and practical enlightenment for the government to promote the green development transformation of enterprises. [ABSTRACT FROM AUTHOR]

A major goal of the "14th Five-Year Plan" phase is to promote the green transformation of industrial enterprises to address the 'dual carbon' challenge. Utilizing the China Industrial Enterprises Database and the Polluting Enterprises Database, this paper calculates the carbon emissions of Chinese industrial enterprises from 2001 to 2010 at the micro level. It presents an analysis of the heterogeneity of carbon emission efficiency (TPI) in industrial enterprises, as well as the factors influencing corporate TPI. This study finds that enterprises within a subdivided industry exhibit heterogeneous levels of TPI, with carbon emissions largely affected by the structure of energy consumption. The researchers suggest accelerating the transition of industrial enterprises to green technology and argue that carbon emission policies should shift from controlling direct total targets to strengthening market-oriented policy tools. Carbon reduction targets should be more stringent for enterprises with lower TPI, considering the heterogeneity among enterprises. To meet the challenges of emission reduction, industrial enterprises are encouraged to actively reform their energy consumption structure. Government policies should aim to reduce clean energy costs and encourage the use of clean energy by industrial enterprises. [ABSTRACT FROM AUTHOR]

Combine harvesters (CHs) use large amounts of diesel fuel and they contribute significantly to greenhouse gas (GHG) emissions and air pollutants. A reduction in fuel consumption of agricultural machines has immediate economic benefits but can also results in longer term environmental benefits. Savickas et al. (Precis Agric 2023. https://doi.org/10.1007/s11119-023-10060-6) introduced extensive telematics data analysis and field tests for identifying the potential sustainable use of CHs. This paper follows on from that research and discusses the need for an IT tool for the management of economic and environmental indicators of a CH. The innovative tool allows the comparison of the data of specific agricultural machines with the multi-year telematics data stored in the database. It offers opportunities to reduce environmental impact and evaluate the results of field trials, presenting them in terms of environmental impact, expressed as CO2 equivalents. [ABSTRACT FROM AUTHOR]

The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered. [ABSTRACT FROM AUTHOR]

The Danube navigation is geographically limited and subject to the effects of environmental change. On the other hand, the Danube commercial navigation is the point source of pollution with emissions of the Danube vessels and harbours, directly polluting the environment. The focus of this paper is the annual evaluation and calculation of air polluting emissions from fossil fuel consumption from the following types of the Danube fleet: push boats, pull boats, freight motor vessels and cruise passenger ships. Emissions from the Danube transport vessels are estimated by EMEP/EEA (European Monitoring and Evaluation Programme/European Environment Agency) air pollutant emission inventory guidebook within Serbian-Croatian, Serbian and Serbian-Romanian sections of the Danube fairway on an average level. Emissions costs for all types of the Danube fleet (euro per vessel-kilometre) are estimated by the Practical guideline for realistic transport pricing depending on the population density close to the waterway: rural or urban area. [ABSTRACT FROM AUTHOR]

The aim of this study was to measure and model the fuel consumption of cut-to-length (CTL) logging machinery in cutting and forwarding under Nordic harvesting conditions, and to clarify which factors had the greatest effect on the fuel consumption. In addition, the total annual fuel consumption and CO2eq emissions were calculated for CTL logging operations in Finland in 2020. The data were collected during a long-term follow-up study on the fuel consumption of harvesters and forwarders conducted between March 2018 and April 2019. The fuel consumption data were obtained from a total of 16 harvesters and 13 forwarders equipped with digital flow metres featuring an accuracy of ± 1% of the fuel volume measured. The engine power of the forest machines explained most of the hour-based fuel consumption. Correspondingly, the harvesting conditions of the forest stand best explained the cubic-metre-based fuel consumption. The fuel consumption of CTL logging operations (cutting and forwarding) averaged 1.4 L m−3 in final felling, and 3.1 and 2.2 L m−3 in first and later thinning, respectively. There was a large variation in the cubic-metre-based fuel consumption between individual machines, both for harvesters and forwarders. The total calculated fuel consumption in Finnish CTL operations in 2020 was 126.6 million L, with the calculated CO2eq emissions totalling 334,209 t (i.e. 5.7 kg m−3). Several measures to accelerate fuel and energy efficiency and reduce CO2eq emissions in CTL logging operations are discussed in the paper. [ABSTRACT FROM AUTHOR]

This study analyzed the effect of lane-changing behavior on traffic flow emissions and energy consumption of road sections in fuel vehicle-battery electric vehicle (FV-BEV) and human-driven vehicle-cooperative adaptive cruise control (HDV-CACC) multi-dimensional mixed traffic flow environments. Based on the traditional energy consumption model, a multi-dimensional mixed traffic flow energy consumption model was established by considering the BEV and CACC penetration rates. The microscopic traffic flow theory approach was used to analyze lane-changing behavior and the influencing mechanism of lane-changing behavior on the energy consumption of multi-dimensional mixed traffic flow, and MATLAB was used for the experimental simulation. The lane-changing behavior of the leading vehicle had a negative impact on the energy consumption of road segment traffic flow. Within the 95% effective impact range, the average energy consumption of traffic flow with respect to lane-changing behavior was 7.8% higher than that of the following traffic flow. The BEV penetration rate was beneficial for reducing the energy consumption of mixed traffic flow. At an economic velocity, the energy consumption of homogeneous BEV traffic flow was only 58.3% of that of homogeneous FV traffic flow. The CACC penetration rate could increase the traffic flow toughness. When the BEV penetration rate was constant, the higher the CACC penetration rate, the smaller the impact of lane-changing behavior on emissions. When traffic flow was completely transformed to homogeneous CACC traffic flow, lane-changing behavior only increased the overall energy consumption of the traffic flow by 4.99%, which was lower than the average level. Consequently, the promotion of BEV and CACC can improve the impact of traffic emissions on air pollution. When CACC penetration is low, reducing unnecessary lane-changing behavior to ensure the stability of traffic flow is also an effective way to reduce emissions. Implications: Multi-dimensional mixed traffic flow energy consumption model is proposed. CACC penetration rate, BEV penetration rate and lane-changing behavior will change traffic energy consumption. In this paper, different influencing factors are analyzed one by one. It provides a theoretical basis for relevant departments of traffic management to optimize vehicle emissions and traffic organization. [ABSTRACT FROM AUTHOR]

In the search for sustainable processes and products, eco-friendly policies have been developed over the years, aiming to reduce the environmental impacts as a step toward sustainability. Among the environmental impacts, alternatives to mitigate the greenhouse gas emissions - GHG stand out due to the concerns with climate change. Then, the development and use of renewable resources become relevant. Considering that the supply chains are intense in energy consumption and GHG emissions (since it involves processes related to supply, production, transport, consumption), it becomes relevant to investigate if the management of sustainable supply chain is considering the renewable energies in their processes. Therefore, this paper aims at mapping the role of renewable energies in the context of the sustainable supply chain, analyzing the literature published at Web of Science database - WoS about the subject. The main researchers, organizations, collaboration networks were presented, and the 21 most cited studies were mapped in this paper. The research was carried out with the papers published at WoS until 2019, using VantagePoint software to handle information. The findings show that the research about renewable energy in the context of a sustainable supply chain has been growing, especially since 2010. Moreover, biomass, biofuels and photovoltaic energy were the recurrent sources of renewable energy studied by most cited papers. However, the theme presented itself as new and there is still potential to be explored. [ABSTRACT FROM AUTHOR]

Global warming is one of the most important environmental problems in the world. One might state that the most potential considerable cause of global warming is the increase in greenhouse gas emissions in the atmosphere. The commonly suggested method used to struggle with this potential problem is to consume renewable energy sources instead of fossil fuels. In today's World, where the rate of urbanization is very high, the research on the effect of the energy demand of the residential sector on global warming has become a crucial research topic in the energy and climatic literature. Among different solar sectors, the residential sector is a major energy consumer. Considering the residential, commercial, and industrial solar sectors, the residential solar sector has become a major component of total solar energy. It accounts for approximately 43% of total solar. On the other hand, to the best of my knowledge, there exists no empirical study in the literature considering the influence of residential solar consumption on environmental quality. Thus, determining the effect of the most used energy source, residential solar, energy consumption on the environmental quality has arisen the key research problem of this paper. This research analyzes the effects of residential solar energy usage on CO 2 emissions for the period 1989:1–2020:1 in the USA. It has been observed throughout Morlet wavelet analyses that the use of residential solar energy has a strong effect on mitigating CO 2 emissions in the 1–2 year frequency. Eventually, this paper provides relevant potential energy policies that can enhance the efficiency of residential solar energy consumption. Partial wavelet coherence (RSolar-CO 2 || Coal, Hydro, IP), 1989:1–2020:1. [Display omitted] • Considers the impact of residential solar consumption (RSolar) on CO 2 emissions. • Follows the data for the USA for the monthly period 1989:1–2020:1. • Launches wavelet models in which RSolar, CO 2 , Coal, Hydro, and IP are employed. • Reaches the negative impact of RSolar on CO 2 in the USA for the period 1989:1–2020:1 at 1-2 year-cycle period. • Through potential energy policies, residential solar energy consumption efficiency should be increased. [ABSTRACT FROM AUTHOR]

The process of Land Consolidation (LC) is deemed an important instrument of rural development in many countries, where it contributes to the economic development and viability of their rural areas. This paper aims to analyze three areas of Castilla y León in Northwestern Spain, all having similar agronomic features. The above areas have recently undergone LC processes. This research proves LC can contribute to reducing greenhouse gases (GHG) using the rationalization introduced in the layout of the agrarian exploitations. For this purpose, this paper analyzes the effects of LC actions on the size, shape, and level of scattering of the owners affected and compares the energy consumption in every journey from the exploitation to each plot, as well as the number of row-end turnings within the plots. GHG reductions present significant differences depending on the LC technique carried out, the size of the area consolidated, and the different degrees of intensification of agricultural exploitations. Through the three LC projects analyzed, a reduction in GHG emissions of 23.60% in SA1, 11.46% in SA2, and 9.85% in SA3 would have been obtained. In total, 1067.34 t CO2 of GHG emissions would have been mitigated. In the light of the results obtained, LC can be considered an efficient process in the strategy of GHG reduction, all in line with the current commitments derived from the Paris Agreement. It is also necessary to continue to research the structure and importance of the consumption of fossil fuels in farming and its possible link to emission reduction policies. [ABSTRACT FROM AUTHOR]

The rapid growth of information technology and industrial revolutions provoked digital transformation of all sectors, from the government to households. Moreover, digital transformations led to the development of cryptocurrency. However, crypto trading provokes a dilemma loop. On the one hand, crypto trading led to economic development, which allowed attracting additional resources to extending smart and green technologies for de-carbonising the economic growth. On the other hand, crypto trading led to intensifying energy sources, which provoked an increase in greenhouse gas emissions and environmental degradation. The paper aims to analyse the connections between crypto trading, economic development of the country, renewable energy consumption, and environmental degradation. The data for analysis were obtained from: Our World in Data, World Data Bank, Eurostat, Ukrstat, Crystal Blockchain, and KOF Globalisation Index. To check the hypothesis, the paper applied the Pedroni and Kao panel cointegration tests, FMOLS and DOLS panel cointegration models, and Vector Error Correction Models. The findings concluded that the increasing crypto trading led to enhanced GDP, real gross fixed capital formation, and globalisation. However, in the long run, the relationship between crypto trading and the share of renewable energies in total energy consumption was not confirmed by the empirical results. For further directions, it is necessary to analyse the impact of crypto trading on land and water pollution. [ABSTRACT FROM AUTHOR]

The paper discusses existing trends and prospects for decarbonization in the Commonwealth of Independent States (the CIS), an international organization that regroups Armenia, Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, and Uzbekistan. The CIS occupies a significant share of Eurasia, representing a large share of global energy consumption and production with a corresponding carbon footprint. These countries and their decarbonization prospects are rarely discussed in the English-language scientific literature. This paper fills in this gap by offering a comprehensive analysis based on statistical data, policy documents, and scenario-based future projections. The results underline that revisiting Nationally Determined Contributions, increasing energy efficiency, and decoupling GDP growth from greenhouse gas emissions are essential to the implementation of the Paris Agreement. The future energy mix should include larger shares of renewable energy, hydrogen, fossil fuels (highly efficient with carbon capture), and nuclear energy to achieve energy security and decarbonize the economy of the region in the long term. [ABSTRACT FROM AUTHOR]

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research. • An energy storage model is proposed to address the shortage of energy storage in waste heat trading in industrial parks. • A coordinated scheduling program is presented to reduce energy scheduling imbalances between multiple campuses. • The carbon-constrained energy storage device is integrated to improve the decarbonization capacity. [ABSTRACT FROM AUTHOR]

In order to explore the comprehensive assessment method of air quality of pollutant emission of energy consumption in China's urban internal industrial parks, based on the full consideration of the actual situation of air pollution control in Nanjing HX Industry Park, this paper selects 15 air quality assessment indicators. According to the national air quality standard, the air quality standard of Nanjing and the air quality standard of Nanjing HX Industry Park, the air quality assessment standard is determined about pollutant emission of energy consumption in Nanjing HX Industry Park. Through the technical treatment of the selected assessment indicators, the niche suitability model is introduced, and based on this, the spatial niche suitability model is reconstructed to comprehensively assess the air quality of pollutant emission of energy consumption in Nanjing HX Industry Park. The study found that the air quality of Nanjing HX Industry Park has been improved from level III in 2012 to level I in 2020. According to the assessment results, the differences of assessment results of different niche suitability models are discussed, which fully reflects the advantages of spatial niche assessment methods. The change law and influencing factors of the assessment results are further discussed. [ABSTRACT FROM AUTHOR]

Ireland has significantly increased its climate mitigation ambition, with a recent government commitment to reduce greenhouse gases by an average of 7 %yr-1 in the period to 2030 and a net-zero target for 2050, underpinned by a series of 5-year carbon budgets. Energy systems optimisation modelling (ESOM) is a widely used tool to inform pathways to address long-term energy challenges. This article describes a new ESOM developed to inform Ireland's energy system decarbonisation challenge. The TIMES-Ireland Model (TIM) is an optimisation model of the Irish energy system, which calculates the cost-optimal fuel and technology mix to meet future energy service demands in the transport, buildings, industry, and agriculture sectors, while respecting constraints in greenhouse gas emissions, primary energy resources, and feasible deployment rates. TIM is developed to take into account Ireland's unique energy system context, including a very high potential for offshore wind energy and the challenge of integrating this on a relatively isolated grid, a very ambitious decarbonisation target in the period to 2030, the policy need to inform 5-year carbon budgets to meet policy targets, and the challenge of decarbonising heat in the context of low building stock thermal efficiency and high reliance on fossil fuels. To that end, model features of note include future-proofing with flexible temporal and spatial definitions, with a possible hourly time resolution, unit commitment and capacity expansion features in the power sector, residential and passenger transport underpinned by detailed bottom-up sectoral models, cross-model harmonisation, and soft-linking with demand and macro models. The paper also outlines a priority list of future model developments to better meet the challenge of deeply decarbonising energy supply and demand, taking into account the equity, cost-effectiveness, and technical feasibility. To support transparency and openness in decision-making, TIM is available to download under a Creative Commons licence. [ABSTRACT FROM AUTHOR]

Reducing air pollutant emissions and energy consumption, as a necessary step to make ports more sustainable, is one of the crucial tasks and challenges of port management. Some of the port strategies to meet the term “green port” usually include reducing fuel consumption from vessels and vehicles in ports. This paper estimates the emission inventory of maritime traffic for the cargo Port of Zadar. For this research, emissions from cargo ships are estimated in the period from January 01. 2018 until October 01. 2019. The “bottom-up” methodology has been applied for estimating emissions, which includes detailed data on the ship’s characteristics (engine power, the load factor, fuel type, the emission factor) and time spent cruising and hotelling. The emissions from cargo ships have been estimated for three ship’s activities: cruising in the reduced speed zone, hotelling (time spent at berth), and maneuvering. The emission results (tons/year) refer to the pollutants such as nitrogen oxides (NOx), sulphur oxides (SOx), particulate matter (PM), volatile organic compounds (VOC), and carbon dioxide (CO2) which represents greenhouse gases. Estimating emission inventory is the first step for planning effective port air quality control. Some recommendations for reducing emissions in port areas are emphasized in this paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system's evolution mechanism based on Haken's synergetic theory to supplement a systematic perspective's empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution. [ABSTRACT FROM AUTHOR]

To achieve the carbon neutrality goal, the industrial parks with heavy multi-energy consumption are gradually promoting green and low-carbon transitions. This transition is a long-term dynamic evolution process, which is affected by the strategies of different stakeholders and unpredictable events. It will lead to the industrial parks blindly promoting carbon emission reduction tasks. Therefore, it is necessary to analyze the stakeholders' strategy evolution trajectories based on the evolutionary game theory under changing scenarios to provide correct policy guidance for their carbon emission reduction process. In this paper, firstly, an evolutionary game model of carbon emission reduction in industrial parks is constructed, including an energy supplier, an industrial park operator, and the government. Secondly, the dynamic behavior characteristics of different stakeholders and the factors affecting the stability of the strategy equilibrium are analyzed thoroughly. Finally, different scenarios are formulated to simulate and analyze industrial parks' long-term dynamic evolutionary game law of carbon emission reduction. Furthermore, discussions and suggestions are provided to guide industrial parks' future carbon emission reduction transitions. [ABSTRACT FROM AUTHOR]

IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate. [ABSTRACT FROM AUTHOR]

The path towards decarbonization requires a progressive adaptation of all refrigeration systems, but only stationary ones have been intensely studied to improve their environmental performance. However, refrigerated transport is vital in the cold chain and must be considered in the green transition. In this paper, we propose a model for a hybrid refrigerated van that includes photovoltaic panels and electric batteries to decrease total greenhouse gas emissions from the engine. Thermal, electrical, and battery sub-models are considered and integrated into the comprehensive hybrid solar-powered refrigerated van model. Different technologies are compared, including lithium and lead-acid batteries and three different types of photovoltaic panels. The model was validated regarding van fuel consumption, showing a 4% deviation. Single and multiple delivery scenarios are considered to assess the energy, economic, and environmental benefits. Monthly CO2,e emissions could be reduced by 20% compared to a standard refrigerated van. Despite the environmental benefits provided by this sustainable solution, the payback period is still too long (above 20 years) because of the necessary investment to adapt the vehicle and considering fuel and electricity prices currently. [ABSTRACT FROM AUTHOR]

Intracting as a financing instrument for energy saving measures in university buildings Intracting creates positive conditions for the energy management to continuously improve the energy efficiency of university buildings. To start Intracting first energy saving measures implemented are financed by a given start up financing. The energy cost savings achieved are credited to an intracting‐account and reinvested in further energy saving measures. This leads to more cost savings which are reinvested again and so on. If properly designed, this "Intracting cycle" creates an exponential increase of the available finances. The energy saving investments within 15 years can reach up to 20 times the start up financing. Therefore, energy consumption and CO2‐emissions can be reduced substantially. The research project "intracting at universities – IntrHo" examines the implementation of Intracting at universities and develops the tools necessary for planning and application. Fundamental results are presented in this paper. They are largely transferrable to municipalities. [ABSTRACT FROM AUTHOR]

With the objective to reduce fossil fuels consumption and greenhouse gas emissions, the integration of solar thermal energy into energy-intensive processes represents the most effective solution. This paper investigates the best solar integration scenarios to an industrial dryer unit through an exergy analysis. The exergetic balance for each subsystem is conducted, and a sensitivity parametric analysis was carried out. The optimization using the Genetic Algorithm was then conducted to define the optimum solar operating conditions. The exergetic evaluation showed an important exergy destruction at the dryer and combustion chamber, accounting for 58% and 42% of the inlet exergy flow, respectively. The sensitivity analysis outcomes showed that the exergy efficiency is clearly increased by increasing both drying temperature, decreasing the inlet moisture content, with preheating the combustion air temperature. Considering these variables, the optimization problem enabled enhancing the exergy efficiency by 40% and 30% for the overall process and flash dryer, respectively, when increasing drying temperature to 850 °C, an inlet moisture content of 14.9% and a preheated air combustion of 150 °C. Finally, the solar field could be integrated to the industrial dryer to preheat the ambient air for the combustion chamber to the setpoint temperature of 150 °C. [ABSTRACT FROM AUTHOR]

The European Union is aiming at reaching greenhouse gas (GHG) emission neutrality in 2050. Austria's current greenhouse gas emissions are 80 million t/year. Renewable Energy (REN) contributes 32% to Austria's total energy consumption. To decarbonize energy consumption, a substantial increase in energy generation from renewable energy is required. This increase will add to the seasonality of energy supply and amplifies the seasonality in energy demand. In this paper, the seasonality of energy supply and demand in a Net-Zero Scenario are analyzed for Austria and requirements for hydrogen storage derived. We looked into the potential usage of hydrogen in Austria and the economics of hydrogen generation and technology and market developments to assess the Levelized Cost of Hydrogen (LCOH). Then, we cover the energy consumption in Austria followed by the REN potential. The results show that incremental potential of up to 140 TWh for hydropower, photovoltaic (PV), and wind exists in Austria. Hydropower generation and PV is higher in summer- than in wintertime, while wind energy leads to higher energy generation in wintertime. The largest incremental potential is PV, with agrivoltaic systems significantly increasing the area amenable for PV compared with PV usage only. Battery Electric Vehicles (BEV) and Fuel Cell Vehicles (FCV) use energy more efficiently than Internal Combustion Engine (ICE) cars; however, the use of hydrogen for electricity generation significantly decreases the efficiency due to electricity–hydrogen–electricity conversion. The increase in REN use and the higher demand for energy in Austria in wintertime require seasonal storage of energy. We developed three scenarios, Externally Dependent Scenario (EDS), Balanced Energy Scenario (BES) or Self-Sustained Scenario (SSS), for Austria. The EDS scenario assumes significant REN import to Austria, whereas the SSS scenario relies on REN generation within Austria. The required hydrogen storage would be 10.82 bn m3 for EDS, 13.34 bn m3 for BES, and 18.69 bn m3 for SSS. Gas and oil production in Austria and the presence of aquifers indicates that sufficient storage capacity might be available. Significant technology development is required to be able to implement hydrogen as an energy carrier and to balance seasonal energy demand and supply. [ABSTRACT FROM AUTHOR]

Decarbonisation means reducing or ideally eliminating the greenhouse gas emissions by evaluating all processes in a system from a “low-carbon” perspective. This review describes the current state of the fertiliser industry, and the options and recommendations for its decarbonisation. Decarbonisation of the fertiliser industry can be achieved with the developing technologies in terms of raw materials, energy usage, and operating conditions in the fertiliser production processes. Low-carbon fertilisers can be realised with the improvements that can be made in the life cycle of the fertiliser, from the derivation of the raw materials to the formation of the final products. Today, many decarbonisation studies with new technologies that can be created in ammonia production, which is the most energy-intensive process in the fertiliser industry, are at the research stage. This paper reviews the use of hydrogen source in ammonia production and achieving biomass gasification or electrolysis with low carbon footprints, reducing greenhouse gas emissions in nitric acid production, integrating carbon capture and storage technologies into the fertiliser processes, and increasing the efficiency of low-carbon agricultural practices. [ABSTRACT FROM AUTHOR]

The paper presents an assessment of thermal energy consumption for heating in 10 buildings made in the OWT-67N prefabricated large-panel technology from 1983 to 1986. The work covers the years 2002–2020 in three periods: before and after thermal modernization and after the use of an innovative weather prediction heating system control in buildings. The analysis made it possible to assess the impact of carrying out a deep thermal modernization, and then installing a modern forecast regulation system in terms of reducing heat energy consumption for central heating purposes, as well as reducing greenhouse gas emissions, such as CO2, SOx, NOx, CO and benzo(a)pyrene, into the atmosphere. The implementation of deep thermal modernization in buildings allowed for savings of 19.8–35% of thermal energy consumption for heating. The use of additional regulation based on prediction saved from 4.8 to 23.5%, except for one building BU10, where there was an increase in final energy consumption by 2.1%. Replacing the weather regulation in heating stations with the forecast regulation additionally reduced the emission of pollutants by 11.1%, compared to the reduction of pollutants achieved as a result of the thermal modernization of buildings alone, amounting to an average of 29.7%. [ABSTRACT FROM AUTHOR]

The increasingly widespread use of vehicles has intensified fuel consumption and hence the emission of air pollutants, causing a negative environmental impact on both human health and climate change. It is well known that vehicle fuel consumption depends on several factors such as engine and vehicle technology, road characteristics, traffic conditions, and driver ability. Although the relationship between these variables has been subject of several researches, the combined influence of traffic flow with road type on vehicle fuel consumption has not yet been studied in depth. This paper aims to fill this gap by processing a large dataset of real-world driving data from an experiment carried out in Madrid, Spain; and to develop and validate a new approach using cluster analysis to define real traffic conditions. The results indicate that poor traffic conditions can significantly reduce vehicle's energy efficiency and influence driving behavior, rather drastically depending on the road typology. While on high-capacity roads the speed covariance increases up to 73% in congestion, on low-capacity roads it increases by 31%, since road geometry also covers an important role; indeed, due to their complex and segmented geometry, local streets show 37% less vehicle's energy efficiency compared with highways. The outcomes of this study suggest that energy efficiency depends on avoiding congestion on high-capacity roads, selecting green itineraries using the right road sections and having a more homogeneous driving behavior on low-capacity roads, through eco-driving whenever possible. [ABSTRACT FROM AUTHOR]

Clay drying is one of the most important steps in the construction materials industry. Due to its high-energy requirement, clay drying is one of the most energy-consuming processes contributing to greenhouse gas emissions, motivating the search for strategies to increase the energy efficiency of the process. The process involves several operating variables whose historical records can generate a large amount of data, which can be organized and analyzed efficiently using data mining techniques (DMT). In this paper, the performance variables of a material-industry rotary dryer were identified and analyzed. A cluster analysis was performed using the Ward's classification method with 1729 operating data, identifying the kind of materials processed and the operating configurations. A linear regression model was constructed using the RRELIEFF feature selection algorithm in Orange: Data Mining Toolbox in Python. The main thermal-energy-consumption variables were the production rate, power, inlet gas temperature, and product moisture delta. These four variables represent the dryer's specific energy consumption index ( I c ) more suitably than the classic Energy Vs Production model. Consequently, it was established that a global-aeration-factor reduction could improve the process efficiency by 7% to 10% at the average production rate. [ABSTRACT FROM AUTHOR]

In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively. [ABSTRACT FROM AUTHOR]

In the context of smart cities, sustainability is an essential dimension. One of the ways to achieve sustainability and reduce the emission of greenhouse gases in smart cities is through the promotion of sustainable energy. The demand for affordable and reliable electrical energy requires different energy sources, where the cost of production often outweighs the environmental factor. This paper aims to investigate the ways smart cities promote sustainability in the electricity sector. For this, a systematic literature review using the PRISMA protocol was employed as the methodological approach. In this review, 154 journal articles were thoroughly analyzed. The results were grouped according to the themes and categorized into energy efficiency, renewable energies, and energy and urban planning. The study findings revealed the following: (a) global academic publication landscape for smart city and energy sustainability research; (b) unbalanced publications when critically evaluating geographical continents' energy use intensity vs. smart cities' energy sustainability research outcomes; (c) there is a heavy concentration on the technology dimension of energy sustainability and efficiency, and renewables topics in the literature, but much less attention is paid to the energy and urban planning issues. The insights generated inform urban and energy authorities and provide scholars with directions for prospective research. [ABSTRACT FROM AUTHOR]

The excessive carbon emissions not only intensify the global climate change, but also seriously restrict the sustainable development of social economy. However, improving industrial carbon emissions efficiency is the most directly effective way to reduce emissions. Therefore, accurate measurement and analysis of carbon emissions efficiency and evolution characteristics of China's industrial system is the basis for China to improve carbon emissions efficiency. Based on this, we adopted energy consumption method and input-output method to calculate and analyze the industrial carbon emissions efficiency and evolution characteristics of China from 2002 to 2015. The results show that (1) If carbon emissions from cement production are ignored and only energy-related carbon emissions are considered, the calculation results of carbon emissions efficiency of heavy industry will be overestimated about 30%. (2) Compared with 2002, China's industrial carbon emissions efficiency increased by about twice in 2015. Specifically, the optimization of carbon emissions efficiency in agriculture, construction, light industry and service industry is very obvious, which has increased by 5.65, 5.00, 4.69 and 4.68 times respectively; The optimization of carbon emissions efficiency in power sector, heavy industry, transportation and chemical industry was obvious, which increased by 2.55, 2.01, 1.86 and 1.47 times respectively; The carbon emissions efficiency of the fossil energy sector has decreased by 0.36 times. (3) The carbon emissions efficiency of various industries has significant differences. Among them, agriculture, service industry, light industry and construction industry are generally industries with higher carbon emissions efficiency, while power sector, fossil energy sector, transportation industry, chemical industry and heavy industry are generally industries with lower carbon emissions efficiency, especially power and fossil energy sectors. Based on these, this paper provides policy implications and scientific evidence for accurately improving carbon emissions efficiency from the perspective of carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

Concerns about climate change, air pollution, and the depletion of oil resources have prompted authorities to enforce increasingly strict rules in the automotive sector. There are several benefits to implementing fuel cell hybrid vehicles (FCHV) in the transportation sector, including the ability to assist in reducing greenhouse gas emissions by replacing fossil fuels with hydrogen as energy carriers. This paper examines different control strategies for optimizing the power split between the battery and PEM fuel cell in order to maximize the PEM fuel cell system efficiency and reduce fuel consumption. First, the vehicle and fuel cell system models are described. A forward approach is considered to model the vehicle dynamics, while a semi-empirical and quasi-static model is used for the PEM fuel cell. Then, different rule-based control strategies are analyzed with the aim of maximizing fuel cell system efficiency while ensuring a constant battery state of charge (SOC). The different methods are evaluated while the FCHV is performing both low-load and high-load drive cycles. The hydrogen consumption and the overall fuel cell system efficiency are considered for all testing conditions. The results highlight that in both low-load cycles and high-load cycles, the best control strategies achieve a fuel cell system efficiency equal or greater to 33%, while achieving a fuel consumption 30% less with respect to the baseline control strategy in low-load drive cycles. [ABSTRACT FROM AUTHOR]

This description paper presents a detailed and consistent estimate and analysis of exhaust pollutant emissions generated by Chile's road transport activity for the period 1990–2020. The complete database for the period 1990–2020 is available at the following DOI: 10.17632/z69m8xm843.2 (Osses et al., 2021). Emissions are provided at a high spatial resolution (0.01 ∘ × 0.01 ∘) over continental Chile from 18.5 to 53.2 ∘ S, including local pollutants (CO; volatile organic compounds, VOCs; NOx ; PM 2.5), black carbon (BC) and greenhouse gases (CO 2 , CH 4). The methodology considers 70 vehicle types, based on 10 vehicle categories, subdivided into 2 fuel types and 7 emission standards. Vehicle activity was calculated based on official databases of vehicle records and vehicle flow counts. Fuel consumption was calculated based on vehicle activity and contrasted with fuel sales to calibrate the initial dataset. Emission factors come mainly from the Computer programme to calculate emissions from road transport version 5 (COPERT 5), adapted to local conditions in the 15 political regions of Chile, based on emission standards and fuel quality. While vehicle fleet grew 5-fold between 1990 and 2020, CO 2 emissions have followed this trend at a lower rate, and emissions of air local pollutants have decreased due to stricter abatement technologies, better fuel quality and enforcement of emission standards. In other words, there has been decoupling between fleet growth and emissions' rate of change. Results were contrasted with global datasets (EDGAR, CAMS, CEDS), showing similarities in CO 2 estimations and striking differences in PM, BC and CO; in the case of NOx and CH 4 there is coincidence only until 2008. In all cases of divergent results, global datasets estimate higher emissions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to calculate the fuel consumption and emissions of carbon monoxide (CO), nitrogen oxide (NOx) and hydrocarbons (HC) in the taxi-out period of aircraft at the International Diyarbakir Airport in 2018 and 2019. Design/methodology/approach: Calculations were performed by determining the engine operating times in the taxi-out period with the flight data obtained from the airport authority. In the analyses, aircraft series and aircraft engine types were determined, and the Engine Exhaust Emission Databank of the International Civil Aviation Authority (ICAO) were used for the calculation. Findings: Total fuel consumption in the taxi-out period in 2018 and 2019 was calculated as 525.64 and 463.69 tons, respectively. In 2018, HC, CO and NOx emissions caused by fuel consumption were found to be 1,109, 10,668 and 2,339 kg, respectively. In 2019, the total HC, CO and NOx emissions released to the atmosphere during the taxi-out phase are 966, 9,391 and 2,126 kg, respectively. B737 Series aircraft have the largest share in total fuel consumption and pollutant emissions. Practical implications: This study explains the importance of determining fuel consumption and pollutant emissions by considering engine operating times in the taxi-out period. The study provides aviation authorities with scientific methods to follow in calculating fuel consumption and emissions from aircraft operations. Originality/value: The originality of this study is the calculation of fuel consumption and pollutant emissions by determining real-time engine running times in the taxi-out period. In addition, calculations were made with real engine operating times determined in the taxi-out period using real flight data. [ABSTRACT FROM AUTHOR]

The rapid advancements in the technology, increase in comfort levels, movement of population to urban areas, depletion of fossil fuels and increasing greenhouse gas emissions have invigorated the use of renewable energy resources for power generation in the last few years. The major renewable energy resources which have potential to fulfill the requirements includes solar energy, wind energy, small hydro and biomass etc. Among these major resources, solar energy-based technology is considered as one of the fastest growing technology because of its various advantages and ubiquitous availability of the resources. However, there are certain challenges in the utilization of solar energy for power generation because of various uncertainties in the atmosphere. As a result, the power generated from solar based power plants is fluctuating in nature which is not desirable. Therefore, the utilities are adopting the smart grid approach which has ability to integrate the solar power plants efficiently and the solar energy forecasting is one of the essential tools for this new model. In this paper, AI based techniques are utilized to forecast solar energy using high quality measured solar irradiance data. The forecasting accuracy of the developed models is evaluated based on statistical indices such as absolute relative error and mean absolute percentage error. The results obtained from the developed models are compared to observe the forecasting ability and performance with the high-quality measured data and found accurate. [ABSTRACT FROM AUTHOR]

In this study, the optimization of a multisource hybrid photovoltaic (PV)/Wind/Diesel/Fuel cell (FC) system is performed to meet three realistic loads demand for heavy, medium and small activities observed at Figuil, Cameroon. This paper also examines and compares the techno-economic viability of an off-grid hybrid PV/Wind/Diesel/FC, PV/Diesel/FC and Wind/Diesel/FC systems in terms of net present cost (NPC), cost of energy (COE) and environmental emissions. The effectiveness of different evolutionary algorithms employed in the designing problem of the various hybrid systems is also investigated. The results revealed that, for maximum loss of power supply probability (LPSP) of 5% and minimum renewable energy fraction of 85%, the PV/Wind/Diesel/FC system is economically the best choice for supplying the three types of non-domestic loads demand with corresponding NPC, COE and LPSP values of 159319.4$, 1.087$/kWh and 0.0397 for heavy activity; 150185.8$, 1.753$/kWh and 0.0411 for medium activity; and 144588.0$, 2.384$/kWh and 0.0288 for small activity, respectively. The results also demonstrated that, in terms of pollution emissions minimization, the PV/Wind/Diesel/FC configuration is a preferable option, with an overall yearly CO 2 emissions of 2946.1 kg, 2043.6 kg and 1504.2 kg corresponding to an annual total fuel consumption of 1115.9L, 774.1L and 569.8L for heavy, medium and small activities, respectively. [ABSTRACT FROM AUTHOR]

Over the past decades, China's vast regional disparities in resource endowment, economic development, and industrial structure have driven large-scale relocations of high energy-consuming industries (RHEIs). Despite extensive research on broad-based industrial relocation and emission transfer, the trend, structure and environmental impact of China's RHEIs need further investigation. This paper employs the multi-regional input-output model (MRIO) to calculate the trend, scale, and structure of the RHEIs among China's 30 provinces. Additionally, we investigate the RHEIs' effect on green total factor energy efficiency (GEE) and its heterogeneity. Our results indicate that the net inflow of high energy-consuming industries decreases GEE, with a more noticeable effect observed in central provinces and after the execution of the Belt and Road Initiative (BRI). However, the positive effect of RHEIs on GEE in outflow areas is more substantial than their negative impact in inflow areas, suggesting that RHEIs can enhance overall efficiency. This study contributes to the understanding of China's industrial relocation by providing valuable insights into China's RHEIs. • China has a large-scale relocation of high energy-consuming industries. • The relocation of China's high energy-consuming industries is calculated. • The relocation's impact on green total factor energy efficiency is analyzed. • Regional heterogeneity is found among different provinces. [ABSTRACT FROM AUTHOR]

This research study focuses on biomass-based cogeneration power plants. Mechanisms have been adopted to promote and regulate project implementation of cogeneration power plants, improve or increase their energy efficiency, and meet environmental standards, reducing local air pollution and CO2 emissions. This paper aims to identify the parameters to monitor and assess the improvement in energy efficiency and reduce CO2 emissions from cogeneration plants. A survey study was first conducted for ten sugarcane processing plants with SPP (10-90 MW) cogeneration plants in the central and northern regions of Thailand. The survey identified parameters used to monitor and control various plant operating conditions such as operating pressures, temperatures, and steam generation flow rates, including preventive maintenance plans to maximize the overall performance of the power plant. A field case study was then conducted for a 25 MW cogeneration power plant in Nakhon Sawan province. The case study involved analyzing the parameters used for monitoring and assessing the best practice operations of a co-generation power plant and evaluating the use of these parameters in analyzing the impacts on the energy efficiency and the CO2 emissions from the co-generation plant. The case study analysis showed that these parameters used for monitoring and controlling the operation of the cogeneration power plants can also be used to monitor and assess the improvement in the energy efficiency of the power plants, and to assess the CO2 emission reductions achieved by the power plant. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO 2 , and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality. [Display omitted] • Description of an air quality modeling chain for decision-making. • Integration of socio-demographics parameters in the air pollutant emissions and concentrations modeling. • Validation of the modeling chain on Paris megacity. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality goals are proposed by the Chinese government, which aims to reach the highest level of CO 2 emissions by 2030 and achieve carbon neutrality by 2060. It is of great significance to accelerate green production and lead high-quality development. Energy carbon emissions account for 88% of total CO 2 emissions, while forest carbon sequestration account for 80% of China's terrestrial ecosystems. In this paper, we analyzed the spatial and temporal evolutions of industrial energy consumption carbon emissions and forest carbon sequestration of Hubei Province, then demonstrated the offsetting effect of forest carbon sequestration on the industrial CO 2 emissions. Our results documented energy carbon emissions and forest carbon sequestration in Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr. Forest carbon sequestration could offset energy carbon emissions before 2005, while the offsetting effect was weak due to the continuous increase in industrial energy consumption after 2005. Significant spatial heterogeneity was observed in both energy carbon emissions and forest carbon sequestration. Wuhan had the biggest carbon emission with annual average carbon emission of about 20.046 Mt, while Enshi had the biggest carbon sequestration with annual average forest carbon sequestration of about 14.411 Mt. Spatial autocorrelation between energy carbon emissions and forest carbon sequestration was significant in Hubei Province in the past two decades. Our findings provided evidence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures. [Display omitted] [ABSTRACT FROM AUTHOR]

Hybrid diesel multiple units (HDMU) are considered an effective way to reduce fuel consumption and pollution emissions in regional non-electrified railways. To minimize the fuel consumption of HDMU, this paper formulates an integrated optimization of driving strategy and energy management. By introducing convex relaxation and linearization, we propose a second-order cone programming method to solve this integrated optimization. Case studies of multiple scenarios validate the effectiveness and robustness of the proposed method. In addition, the simulation results show that compared with the sequential optimization with the step-by-step improvement of driving strategy and energy management, the integrated optimization considers the battery characteristics when optimizing the driving strategy and reduces the electric braking power of the train, thus allowing the battery to absorb more braking energy and reduce fuel consumption. • Integrated optimization of driving strategy and energy management for hybrid diesel multiple units. • An optimization method based on second-order cone programming is proposed. • Compared with sequential optimization, the fuel consumption of integrated optimization is reduced by an average of 6.3%. • Different methods for solving integrated optimization are compared and analyzed. • Optimal driving strategies and energy management with different travel times are compared. [ABSTRACT FROM AUTHOR]

• A dynamic time-delay calculation algorithm based on MI and adaptive sliding window (MIDTA) is proposed. • MIDTA algorithm is used to solve the problem of long time-delay of cement kiln. • An algorithm of residual network and bidirectional novel gated recurrent unit (ResNet-BiNGRU) is proposed. • A Gaussian-weighted multi-model fusion method is proposed. • MIDTA-ResNet-BiNGRU multi-model fusion algorithm can be used to measure the temperature of cement rotary kiln with high precision. China's cement production energy consumption accounts for more than 10% of the country's total energy consumption, and its emissions account for about 60% of the country's industrial emissions. The temperature of a cement rotary kiln directly affects the quality of the final product, the energy consumption of a plant, and emissions. Therefore, it is necessary to measure, to a high degree of accuracy, the temperature of a cement rotary kiln to reduce energy consumption, emissions, and improve product quality. However, the conditions in cement kilns are complex and variable; and there are large time-delays, while temperatures are affected by historical working conditions, making temperature measurement extremely difficult. In this paper, a modelling algorithm with a residual network and a bidirectional novel gated cycle unit is used to predict the clinker exit temperature of the rotary kiln. In addition, to solve the problem of large time-delays in cement kilns, a dynamic time-delay calculation algorithm based on Mutual information and adaptive sliding windows is proposed for time series data reconstruction, and finally a Gaussian-weighted multi-model fusion is applied to the prediction results for cement rotary kilns that produce a wide range of working conditions. To verify the advantages and feasibility of the proposed method, experimental validation is carried out using original production data from a large cement manufacturing enterprise in South China. The results demonstrate that the single step prediction time of the presented approach is 0.66 s, which can predict cement kiln temperatures quickly and accurately, compared to a single conventional forecasting algorithm, the average temperature difference dropped from 11.76 °C to 3.51 °C, R2 increased from 0.43 to 0.89, 94% of the depicted temperature errors are at ± 10 °C This will provide an effective solution for online decision-making during actual cement production. [ABSTRACT FROM AUTHOR]

This paper studies forecasting the use of renewable energies in the water and wastewater industry. Different methods have been proposed to show energy recovery techniques and the use of renewable energy in wastewater treatment. The results indicate that increasing the carbon tax to more than 30 USD per ton of CO2 could reduce emissions from all fossil-fuel power plant types by 30%. • Providing a new assessment approach in applying renewable energy in the water and wastewater industry • Measuring the potential of using renewable energy in the water and wastewater industry • Investigating the use of renewable energy to reduce greenhouse gas emissions and air pollution control by wastewater treatment [ABSTRACT FROM AUTHOR]

In the near future, the railway is seen as the most important solution for reducing greenhouse gas emissions due to transportation. Investment in this sectors are ongoing, in order to increase both the level of service and energy efficiency. The adoption of recent technological solutions, as storage systems or reversible substations, could foster the efficiency, increasing the capability of recovering braking energy. The braking energy is very difficult to measure, it requires installing expensive measurement systems on board trains. In a previous paper the authors already faced this issue developing an accurate technique usable off-line. In the present paper a simplified method, easy to be implemented on train control unit, based only on already available data, is proposed. The paper describes the phenomena of interest and related measurement issues. The analytical model for the power measurement together with a deep analysis on the input quantities are presented. The uncertainty analysis of the proposed method is presented and the uncertainty budget is performed on a real test case. The metrological performances of the method show suitable accuracy level for train energy management. [Display omitted] • Recovering braking power of trains allow to improve the efficiency of the rail system. • Quantifying amount of energy recoverable is fundamental to dimension unconventional substations. • A simplified method for accurate energy measurement of pulsed waveforms is developed. • This method achieves accurate results using instrumentation already present on trains. • The analytical model and an in-depth uncertainty analysis are presented. [ABSTRACT FROM AUTHOR]

Exploiting Chongqing's promotion to a province-level municipality, this paper investigates the causal effect of political hierarchy on corporate SO 2 emissions using a spatial regression discontinuity (RD) design. In 1997, the prefecture-level Chongqing was promoted to a province-level municipality, obtaining a substantial increase in decision-making power over fiscal, personnel and administrative affairs. Given the fact that the areas around the Chongqing-Sichuan border shared the same social, economic, and geographical conditions prior to the treatment, the novel findings show that political hierarchy negatively affects corporate SO 2 emissions and Chongqing's enterprises adjacent to the border have a 26% (1-e−0.301) reduction of SO 2 emissions comparatively. The effects are robust enough using alternative model specifications and bandwidths. Besides, this paper provides empirical evidence that enterprises of Chongqing have resource-receiving and external-financing advantages owing to its higher political status. These interwoven advantages help enterprises in Chongqing exhibit better financial performance and enable them to conduct energy substitution, improve energy efficiency and adopt more end-of-pipe treatment, thereby mitigating SO 2 emissions. Based on these findings, this study provides policy implications by shedding light on the effects of political hierarchy on corporate pollution emissions. • Political hierarchy can shape pollution geography. • Different positions in the political hierarchy lead to disparities in resource-receiving and external financing for firms. • Political hierarchy has negative impacts on corporate SO 2 emissions. • The influencing channels include clean energy substitution, improved energy efficiency and end-of-pipe treatment. [ABSTRACT FROM AUTHOR]

As an effective technology to reduce traffic pollution emissions, the new energy vehicle industry has developed rapidly in recent years, and the sales of new energy vehicles have doubled in 2021. Accurately forecasting methods provide important references for industrial policy deployment, infrastructure construction and energy demand estimation. In order to predict the development trend of China's new energy vehicle industry under the limited data samples, this paper proposes an optimized discrete grey power model to fit the nonlinear relationship between grey information factor and temporal factor. Firstly, the grey differential equation and its discrete form are directly constructed by using power accumulated data, which simplifies the integral solution process of the traditional grey power model. Secondly, fractional accumulation operator is introduced into the discrete model to ensure the new information priority of the original data. Then, the heuristic algorithm is used to accurately estimate the new parameters of the proposed model. Thirdly, the proposed discrete model is the unified form of two existing discrete grey power models, which expands the scope of modeling and has higher adaptability. Simulation experiments and two numerical cases are used to verify the effectiveness of the proposed method. Finally, the proposed method is used to predict the annual sales and ownership of new energy vehicles in China. The predicted results show that by 2025, China's new energy vehicle sales will reach 8.84 million, accounting for about 24% of the total vehicle sales, surpassing the industry development target (20%) set by the Chinese government. [ABSTRACT FROM AUTHOR]

[Display omitted] • Levelised cost of hydrogen for residential heating and mobility was minimised. • European Union targets on greenhouse gas emission reduction were included. • The needed amount of solar photovoltaics and wind turbines was estimated. • Integrating energy service demand patterns decreases system cost. • Wind energy is favourable because of both cost and avoided greenhouse gas emission. Decentralised renewable energy production in the form of fuels or electricity can have large scale deployment in future energy systems, but the feasibility needs to be assessed. The novelty of this paper is in the design and implementation of a mixed integer linear programming optimisation model to minimise the net present cost of decentralised hydrogen production for different energy demands on neighbourhood urban scale, while simultaneously adhering to European Union targets on greenhouse gas emission reductions. The energy system configurations optimised were assumed to possibly consist of a variable number or size of wind turbines, solar photovoltaics, grey grid electricity usage, battery storage, electrolyser, and hydrogen storage. The demands served are hydrogen for heating and mobility, and electricity for the households. A hydrogen residential heating project currently being developed in Hoogeveen, The Netherlands, served as a case study. Six scenarios were compared, each taking one or multiple energy demand services into question. For each scenario the levelised cost of hydrogen was calculated. The lowest levelised cost of hydrogen was found for the combined heating and mobility scenario: 8.36 € kg−1 for heating and 9.83 € kg−1 for mobility. The results support potential cost reductions of combined demand patterns of different energy services. A sensitivity analysis showed a strong influence of electrolyser efficiency, wind turbine parameters, and emission reduction factor on levelised cost. Wind energy was strongly preferred because of the lower cost and the low greenhouse gas emissions, compared to solar photovoltaics and grid electricity. Increasing electrolyser efficiency and greenhouse gas emission reduction of the used technologies deserve further research. [ABSTRACT FROM AUTHOR]