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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

Integrated Energy Systems (IES) play a pivotal role in achieving carbon peak and carbon neutrality goals. However, previous research in IES operation has mainly focused on energy flow constraints, neglecting the impacts of renewable energy output, energy prices, carbon emission, and storage status. To bridge this gap, this paper proposes a Comprehensive Self-Adaptive (CSA) operation strategy as a key component of our two-tier synergistic optimization method, optimizing an IES integrated with Concentrated Photovoltaic/Thermal systems. In the upper-tier capacity configuration, the proposed method considers the full life-cycle carbon emissions of equipment. In the lower-tier operational strategy optimization, the CSA operation strategy automatically adjusts energy generation, utilization, and storage by comprehensively evaluating hourly operating costs, hourly carbon emissions, and hourly storage benefits. Compared to traditional operation strategies and other adaptive methods, the newly developed strategy consistently demonstrates superior performance. When contrasted with separate energy systems, IES operating under the CSA operation strategy markedly stands out, achieving a cost savings rate of 25.87 %, a CO2 reduction rate of 61.54 %, and an electricity matching ratio of 91.24 %. Additionally, the paper explores the performance of the CSA operation strategy across various regions, confirming its feasibility and generalizability under diverse load and irradiance conditions. • An integrated energy system combines CPV/T, ORC, and multi-dimensional energy storage solutions. • A CSA operation strategy holistically assesses costs, carbon emissions, and storage benefits during operation. • A two-tier synergistic optimization method simultaneously addresses capacity configuration and operational strategy. • Comparative studies in various regions to validate the feasibility and generalizability of the CSA strategy and CPV/T-IES. [ABSTRACT FROM AUTHOR]

Dynamic simulation is a powerful tool for accurately evaluating the thermal demands of buildings and assessing the impact of energy refurbishment actions on their final consumption. Conversely, semi-stationary models are widely adopted in commercial applications for its simplified approach, which reduce calculation times, resulting in standardized results showing a certain deviation with respect to the real energy This paper presents the energy and economic comparison between the dynamic simulation and semi-stationary approaches for the calculation of primary energy demand of residential buildings. The semi-stationary method, used by the legislation to calculate the buildings energy label, is based on an energy performance parameter, not representative of the real energy demand. Conversely, an approach based on dynamic simulation provides a more reliable estimation of the primary energy demand. The main novelty of this paper is to numerically prove that the energy and economic savings calculated by means of software based on the current legislation may be overestimated. In this work, the dynamic simulation of the building-plant system is performed by TRaNsient SYstem Simulation (TRNSYS) program. Each building apartment is divided in thermal zones, where the internal heat gains are defined in detail. The semi-stationary simulation of the building-plant system is performed according to the Italian standard UNI TS 11300. The models allow one to evaluate the yearly primary energy demand, along with the energy bill and CO 2 emissions. A specific case study is developed for a residential building located in Naples (Italy). The models are used to calculate the building energy demand for several scenarios, considering different thermal transmittances of the building elements. The results show that the semi-stationary method overestimates of primary energy saving, equal to 64.7 %, with respect to the one calculated with the dynamic approach, equal to 43.2 %. • Semi-stationary and dynamic simulations were compared for residential buildings. • Critical evaluation of energy and economic savings due to the building refurbishment. • Primary energy savings of 65 % and 43 % by semi-stationary and dynamic simulation. • Dynamic simulation is needed within the legal frame of energy efficient buildings. [ABSTRACT FROM AUTHOR]

Faced with the difficult but urgent task of reducing carbon emission intensity, considering urban agglomeration as a fundamental spatial unit for this purpose has become increasingly important. However, as a long-standing and pivotal phenomenon in the development of urban agglomerations, the potential role of spatial functional division in urban agglomerations in reducing carbon emission intensity has been largely overlooked. To fill this gap, this paper explores the relationship between spatial functional division in urban agglomerations and carbon emission intensity, relying on a dataset of 19 urban agglomerations in China. The main results reveal an inverted U-shaped relationship between spatial functional division in urban agglomerations and carbon emission intensity. Spatial functional division in urban agglomerations increases carbon emission intensity in the initial stage; once it reaches a turning point, spatial functional division in urban agglomerations can reduce carbon emission intensity. This conclusion remains robust after conducting various robustness checks and addressing endogeneity concerns. This paper identifies productivity enhancement as an underlying mechanism that shapes this relationship. Moreover, the relationship between spatial functional division in urban agglomerations and carbon emission intensity exhibits heterogeneity based on function types and urban types. These findings emphasize that spatial functional division in urban agglomerations plays a unique role in reducing carbon emission intensity, which offers a new strategy for reducing carbon emission intensity from an individual city level to an entire urban agglomeration level. • An inverted U-shaped relationship exists between SFD and CEI. • Productivity enhancement derived from SFD is a crucial influencing mechanism. • Function types and urban types influence the relationship between SFD and CEI. • Improving SFD adds a novel urban agglomeration-level strategy to reduce CEI. [ABSTRACT FROM AUTHOR]

The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

Growing demand for electricity due to economic development contributes to increased greenhouse gas production, especially CO 2. However, emissions can be limited by enhancing the efficiency of primary energy conversion, such as integrating geothermal energy into coal-fired power plants. Therefore, this paper proposes replacing conventional feed-water heaters with geothermal preheaters to create a hybridized system. This study was based on a numerical model validated at a selected Polish power unit. The model was subsequently calibrated for off-design conditions to facilitate partial load analysis. The obtained characteristics outperformed those of the non-hybrid unit, generating over 18 MW of electric power output. Such an improvement could potentially boost the unit's net efficiency by more than 2.6 %. This enhancement is significant as power units typically operate under part load for approximately 90 % of the time, hence the need to evaluate the performance characteristics of hybridized units in those states. Furthermore, the research outlines the potential decrease in the plant's CO 2 emission factor, with reductions reaching up to 6.5 % under off-design conditions. Based on a gap analysis of the existing literature, this paper's comprehensive partial load evaluation serves as a new addition to research on hybridized systems. • An in-depth analysis of feed-water heaters operation is performed. • Turbine performance calculations for both design and off-design states is validated. • The comprehensive study of hybridized systems integrates three major factors. • Hybrid geothermal-fossil power cycle is vital for specific CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve sustainable development. Whether the digital economy helps or makes it harder to cut emissions is a matter of debate. This paper proposes a new digital economy index based on the entropy weight method. The two-way fixed effect panel model is used to analyze the data of 30 provinces in China from 2011 to 2021. The results suggest that (1) The digital economy can significantly reduce carbon emissions. (2) Improving energy efficiency and promoting energy structure transformation are two essential mechanisms for carbon reduction in the digital economy. (3) There is heterogeneity in the impact of the digital economy on carbon emissions. The digital economy significantly reduces carbon emissions from coal but does not significantly impact carbon emissions from other sources. Moreover, the emission reduction effect of the digital economy is related to regional energy endowment, intelligent manufacturing level, and industrial agglomeration level. This paper provides new empirical evidence for clarifying the relationship between the digital economy and carbon emissions and provides policy implications for promoting the realization of carbon neutrality goals. • The digital economy can significantly reduce carbon emissions. • Energy efficiency and energy structure are two essential mechanisms. • The emission reduction effects of the digital economy vary by region. • The effect is related to intelligent manufacturing and industrial agglomeration. [ABSTRACT FROM AUTHOR]

Under the trend of global carbon neutrality, the integrated energy system with the characteristics of multi-energy scheduling and gradient utilizing will be widely constructed and applied in the future energy market. For the construction of energy systems in emerging building complex, this paper analyzes the load probability characteristics of regional building complex at the planning stage, and conducts an aggregation analysis of its multiple uncertainties, and obtains the conclusion that the load factor at time t obeys the normal distribution. In order to formulate this uncertainty, this paper combines the case study with the scenario analysis method containing scenario generation and reduction to transform the stochastic programming model into several deterministic models and analyses the discrepancies of the optimization results under different objectives and different load variances. The results show that after considering load-side uncertainty, the total system capacity increases by 7 %, the total investment under the minimum investment objective by 4 %, and the carbon emission under the minimum carbon emission objective by 3 %. In addition, the system needs to pay 32.8 % of the total investment increment to obtain 80 % carbon reduction when adopting carbon emissions as the objective. • Mechanism of IES load uncertainty is illustrated. • Scenario analysis is applied to obtain an easy-to-follow MILP model. • The incremental investment due to uncertainty intensity is calculated. [ABSTRACT FROM AUTHOR]

To achieve a win-win situation among multiple stakeholders within the integrated energy system, this paper proposes a low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode. Firstly, an operation framework of integrated energy system based on leader-follower game was established, and a model of integrated energy system operator and user coalition was established considering carbon trading mechanism. Secondly, bi-directional long short-term memory is used to predict the travel data of electric vehicles, and a schedulable capacity model for electric vehicle cluster as shared energy storage was established. Then, an energy trading optimization model based on leader-follower game was constructed for integrated energy system operator and user coalition, and an improved Shapley value is adopted to realize cost allocation within user coalition. Finally, based on the demonstration project of Sino-Singapore Tianjin Eco-City is selected as the research object to make example analysis. The calculation results show this paper can achieve a reasonable benefit allocation among multiple participants, reduce carbon emissions of integrated energy system, and increase renewable energy accommodation capacity. The relevant achievements are a preliminary exploration for an effective mode of sharing economy in integrated energy system. • A low-carbon scheduling strategy of integrated energy system considers Stackelberg game under sharing mode. • A stepped carbon trading mechanism is introduced in the energy price decision of integrated energy system operator. • Bi-LSTM algorithm was used to predict EV cluster data, and Minkowski sum theory was used to get its schedulable capacity. • Everyone in user coalition participants in cooperative game, Individual cost can be allocated by improved Shapley value. • Dichotomy method is used to solve leader-follower game, example uses pilot project of Sino-Singapore Tianjin Eco-city. [ABSTRACT FROM AUTHOR]

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains. [Display omitted] • A model is build for carbon emissions reduction and transport efficiency enhancement. • The model combines route and load rate optimization. • The model achieves carbon emission reductions while improving transport efficiency. • A novel meta-heuristic algorithm for solving the railway heavy-haul trains model. • The proposed algorithm introduces fresh insights for railway heavy-haul trains. [ABSTRACT FROM AUTHOR]

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [ABSTRACT FROM AUTHOR]

Transition to carbon-neutral energy generation is a reality, and great efforts are being made in this direction in the last years. Ammonia is a promising carbon-free fuel, and it can be used as a gas turbine fuel in existing power generation cycles without significant modification. This paper considers various aspects of the use of ammonia as a gas turbine fuel: it provides a review of existing ammonia-fired gas turbines and conducts a study of the prospective technology of using ammonia via thermochemical transformation into hydrogen-rich gas. Moreover, this paper considers on-board hydrogen production technology via thermochemical ammonia transformation. The ammonia fired chemically recuperated gas turbine is analyzed for which the thermal efficiency can be increased up to 5%–7% comparing to traditional gas turbines. Moreover, hydrogen-rich fuel with a hydrogen mole fraction up to 75% is used as a fuel, leading to more stable combustion with lower NO x emission up to 6–10 ppm. Additionally, an approach to on-board hydrogen production from ammonia via the utilization of solar energy is investigated. It is shown that solar energy can replace up to 25% of heat obtained via ammonia combustion. The paper discusses future perspectives in investigations for ammonia-fired gas turbines with on-board ammonia transformation. • Ammonia as a gas turbine fuel with zero CO 2 emission. • NH 3 containing blends show the better combustion and emission performances when pure NH 3. • Thermochemical ammonia transformation as an on-board hydrogen production system is considered. • Use of exhaust gas for ammonia decomposition leads to an increase in efficiency up to 7%. • Use of solar energy for ammonia decomposition can decrease the ammonia consumption up to 25%. [ABSTRACT FROM AUTHOR]

The specific CO 2 production of fuels has commonly been expressed empirically in terms of kg CO2 (kWh)−1. No specifications regarding combustion conditions, transformation technology used, and efficiency are usually presented in detail. In this paper, however, we propose replacing this approach by the use of rigorous chemical engineering calculations based on the composition and the combustion conditions of the fuels. The MS EXCEL program, which is provided as supplementary material to this paper, calculates the specific CO 2 production considering energy losses, adiabatic flame temperature, calorific value and heat of fuel combustion, flue gas temperature, energy losses through flue gas, slag, and the overall energy efficiency of the whole energy conversion process. Results are presented for three fuel groups: coal, hydrocarbons, and renewable fuels. The calculated results are compared with the literature data. A benefit of our approach is that the procedure can be generalized for any fuel of known composition and for different types of combustion-based energy transformation. • Techno-economic energy strategies require accurate knowledge of CO 2 emissions. • The attached chemical engineering program in EXCEL provides the necessary data. • Fuel type and combustion conditions affect CO 2 emission per kWh. • The overall efficiency of various energy production processes was calculated. • The estimated and observed overall efficiencies are in good agreement. [ABSTRACT FROM AUTHOR]

Industrial chain structure optimization and digital transformation are crucial factors for achieving carbon emission reduction goals. Based on China's non-competitive input-output tables, this paper constructed industrial chain panel data using the structural path analysis method. We explored the influence of industrial chain division (ICD) on CO 2 emission intensity through panel fixed effects model, and examined the moderating effect of digitization. The research found that: (1) The ICD can significantly reduce CO 2 emission intensity, the results are robust after some endogeneity tests. (2) The emission reduction effect brought by ICD will be more obvious in high-stage, high-energy consumption, and high-intelligence industrial chains. There is still room to improve the emission reduction brought by the division network effect. (3) Digitization plays a positive moderating role on this effect. Furthermore, digital manufacturing and digital services exhibit heterogeneity in various types of industrial chains. (4) The ICD can reduce carbon emission intensity through pathways such as intelligent transformation, technological innovation, and energy efficiency, and digitization has positively moderated these impact pathways. Based on research results, targeted policy suggestions are provided for optimizing industrial chain structure and promoting digital transformation. • This paper constructed industrial chain carbon emission intensity panel data. • This paper assessed the industrial chain digitization by digital intermediate input. • This paper constructed industrial chain synergistic division (ICSD) index. • This paper explored the influence of ICSD on CO 2 emission intensity. • Digitization plays a positive moderating role on carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO 2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO 2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO 2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment. • An interpretable multi-stage forecasting framework is proposed. • The drivers of energy consumption and CO2 emissions in UK's transport sector are identified. • A comprehensive comparison between different machine learning models is carried out. • The impacts of feature selection methods on machine learning models are investigated. [ABSTRACT FROM AUTHOR]

It is increasing crucial to improve the sustainable use rate of energy and contribute to carbon neutrality and environmental footprints reduction. This work features cutting-edge selected extended papers and developments that were presented at the 25th Conference on Process Integration, Modelling and Optimization for Energy Saving and Pollution Reduction, held on 5–8 September 2022 in Bol, Croatia. More than 400 papers were accepted and presented at this conference. Out of these papers, the 45 contributions in this Virtual Special Issue represent a selection of the most cutting-edge developments in the topic of the Sustainable Use of Energy Contributes to Carbon Emissions Neutrality and Environmental footprint reduction. This paper provides a review of these contributions and above, and groups them into four research themes: (1) Energy Conversion, Storage, Recovery and Efficiency Improvement, (2) Energy Management, Planning and Optimization, (3) Energy, Environment and Economy, and (4) Heat Transfer, Heat Pumps and Heat Convection. [ABSTRACT FROM AUTHOR]

In this paper, 41 prefecture level cities in the Yangtze River Delta (YRD) are selected as the research objects. Based on the semantic mining model, the policy data information from three dimensions namely national, provincial and municipal levels is integrated to build data-driven multiple scenarios. Then the long short-term memory neural network optimized by particle swarm optimization (PSO-LSTM) is further used to predict the peaking time of the YRD urban agglomeration under multiple scenarios. The results show that the YRD urban agglomeration as a whole can achieve carbon peaking on time under all three simulation paths, but the peaking situation of specific cities within the region will be significantly different due to differentiated policy orientations. Under the business as usual scenario, some cities in the north of the region cannot achieve carbon peaking by 2030. Under the stable growth scenario, more cities cannot achieve carbon peaking on time. However, under the low carbon scenario, all cities within the region can achieve carbon peaking by 2030. Therefore, when exploring effective carbon peaking paths, adopting more stringent policies and measures to limit carbon emissions can help achieve the regional carbon peaking goal earlier, ensure that all cities within the region can achieve carbon peaking on time, and can also narrow the gap between cities within the region in terms of peaking time. This paper explores a new scenario analysis parameter setting and carbon emission prediction method, which overcomes the subjectivity of current scenario analysis research in parameter setting to a certain extent, and also provides a scientific reference for predicting the diversified development trend of carbon emission. • The semantic mining method is used to determine the scenario parameters. • PSO-LSTM is used to improve the accuracy of carbon emission prediction. • Multiple scenarios of carbon emission development trends. • Differentiated emission reduction paths for cities. [ABSTRACT FROM AUTHOR]

To reduce the greenhouse gas emissions of the transport sector, synthetic fuels based on CO 2 and H 2 are one viable option. This paper evaluates the integration of a Fischer Tropsch process into a pulp mill to improve efficiency and to lower CO 2 emissions. Different syngas preparation technologies, plant sizes, and gas loop designs are considered. The processes were simulated with a chemical process simulation software to retrieve mass and energy balances. Based on the simulations, the processes are evaluated regarding economics, CO 2 emissions, and efficiency indicators. The Power-to-Fuel efficiency reaches almost 40 % for closed loop configuration with reverse water gas shift reactor for carbon monoxide production. For the open loop configuration the efficiency is tremendously lower. In closed loop configurations, the carbon efficiency reaches 80 %. The levelized cost of syncrude ranges from 3.28 to 7.69 €/kg. The most important impact on syncrude price comes from electricity price, investment cost, and efficiency of water electrolysis. Integrating the Fischer Tropsch process into the pulp mill shows advantages in terms of Power-to-Fuel efficiency and CO 2 emissions but not necessarily in terms of economics. • Integration in terms of mass and energy exchange between pulp mill and Fischer Tropsch process. • Investigation of different plant sizes, syngas preparation routes and gas loop designs of the Fischer Tropsch process. • Levelized cost of syncrude ranges from 3.28 to 7.96 €/kg. • Beneficial effects of the integration into the pulp mill: CO 2 emissions and Power-to-Fuel efficiency. [ABSTRACT FROM AUTHOR]

The current single energy storage approach and market incentives do not fully exploit the potential of diverse energy storage methods and market incentives. Additionally, the traditional operation method often results in a poor alignment between energy supply and demand. Moreover, the conventional solution algorithm struggles to address complex problem-solving involving multiple variables. To address these issues, this paper proposes an improved carbon-neutral energy system (ICNES). Firstly, ICNES enhances residual energy utilization by implementing dual-layer residual power and heat treatment. Secondly, to enhance the effectiveness of market incentives, ICNES incorporates an improved approach that combines a dual-layer carbon capture drive, step carbon penalty, carbon reward, and green certificate trading. Thirdly, to improve the supply–demand matching, ICNES introduces a dynamic hybrid operation approach (DFHL). Finally, the paper proposes a honey badger-pattern search algorithm (HB-PSA) to optimize the configuration of ICNES for the DFHL multivariate scenario. Results indicate that compared to conventional carbon-neutral energy systems, ICNES reduces annual operating costs by 6.686%, CO 2 emissions by 9.538%, primary energy consumption by 12.826%, and energy waste rate by 17.465%. Furthermore, HB-PSA increases the optimization rate from 48.693% to 53.88% of the local optimum. • Dual-layer residual electric and residual heat treatment system is proposed. • Dual-layer carbon treatment for carbon neutrality and carbon profitability. • Carbon markets and green certificate are used in carbon-neutral energy system. • Dynamic hybrid operation to improve the matching of supply and demand. • A honey badger-pattern search algorithm is proposed for solving. [ABSTRACT FROM AUTHOR]

Zero-energy buildings usually achieve annual net-zero energy balance by generating electricity using on-site renewable energies and interconnecting with the power grid. However, the impact of energy mismatch problem in zero-energy buildings design on the grid is often overlooked, which potentially compromises grid's stability. To address this issue, this paper presents an optimization design method based on "grid-friendly interaction" which optimizes the energy system of zero-energy buildings to ensure better alignment between the building's electricity purchase/sale and grid demand. In this study, an indicator of "grid-friendly interaction", which represents the ratio of the net building friendly interaction power to the total annual interaction power, is firstly established to guide buildings in improving their interaction with the grid. Next, the energy supply, demand, and storage schemes of renewable energy systems in the zero-energy building are iteratively adjusted to achieve the best match with grid's expectations. This optimization design method was applied to a practical case in Hong Kong. Results show that, compared to the benchmark building, the optimal system design scheme increases grid friendliness by 83 %, reduces carbon dioxide emissions by 77 %, but also incurs a total cost increase of 180 %. The optimization design method proposed in this paper can effectively mitigate the impact of zero-energy buildings on the power grid, fostering enhanced interplay between the building structures and the grid. The study significantly contributes to the advancement of exploring the potential for energy flexibility in zero-energy buildings, which is of great significance for the safe and stable operation of future grids. The purpose of this study is to explore the energy flexibility potential of zero-energy buildings, which is of great significance for the safe and stable operation of future grids. • A novel indicator that reflects the degree of friendly interaction is proposed. • An optimization design method based on "grid-friendly interaction" is presented. • Single-objective and multi-objective optimization are applied respectively to ZCB. • It is of great significance for the safe and stable operation of future grids. [ABSTRACT FROM AUTHOR]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

For energy conservation, emission reduction and sustainable development, China launched the New Energy Demonstration City Program (NEDC) to try a more decentralized energy governance structure. Based on the theoretical framework between NEDC and carbon emissions, this paper tests its policy impact and mechanism. The results show that NEDC can significantly reduce carbon emissions by about 15.34%. The policy effects of NEDC are characterized by time lag and increasing intensification. NEDC has a stronger effect on reducing carbon emissions in western cities and non-resource-based cities. NEDC can reduce carbon emissions through industrial structure effect, environmental decentralization and information technology effect. NEDC has a synergistic emission reduction effect, which can reduce about 20.39% of nitrogen oxides (NOx) and 18.70% of sulfur dioxide (SO 2). NEDC has policy spillover effect, which can not only reduce the carbon emissions of pilot cities, but also reduce the carbon emissions of neighbouring cities. The space emission reduction effect of NEDC is better within 400 km. Finally, this paper puts forward policy recommendations to strengthen decentralized energy governance to reduce carbon emissions. • NEDC can reduce carbon emissions, which has the characteristics of hysteresis. • Central cities and non-resource-based cities have greater policy effects. • Industry, environmental decentralization, and technology are policy paths. • NEDC has synergistic emission reduction effect, which can reduce NOx and SO 2. • NEDC has spillover effect when the distance is not more than 400 km. [ABSTRACT FROM AUTHOR]

Under the background of "One national network", the balance between the natural gas supply side and the demand side is prominent. This paper proposes a supply-demand balance method of natural gas pipeline network coupled with the hydraulic and thermal characteristics, and decides the transportation scheme through flow rate allocation. This paper designs a two-stage relaxation optimization algorithm, coupled the thermal process into the hydraulic calculation to improve the hydraulic calculation accuracy, making the natural gas transportation scheme more applicable to engineering. The result shows the feasibility of transportation decision-making and the accuracy of hydro thermal calculation of the proposed model through a real case. The relative error of thermal calculation and TGNET is below 2%, and the hydraulic calculation error of coupled thermal can also be within 10%. After optimization, the carbon emissions decrease from 418,100 tons to 260,000 tons, reducing by about 37.81%. This study also analyzes the impact of hydraulic calculation on carbon emissions calculation, and further illustrates the necessity of coupled thermal characteristics in transportation scheme. This study can provide decision support for the transportation operation and help the natural gas pipeline network to meet the dual carbon requirements and achieve sustainable development. • A flow rate allocation method combining operation process is proposed. • A convex relaxation optimization algorithm is designed for energy consumption calculation. • Two stage relaxation iteration algorithm is designed to couple thermal and hydraulic characteristics. • Carbon emission objective is added to the optimization model for environmental benefit. • An actual case is used to prove the practicability of the method. [ABSTRACT FROM AUTHOR]

As a developing region, South America's aviation industry has also developed rapidly, and its carbon emissions have a significant role in achieving carbon neutrality in the global aviation industry. However, the problem of carbon transfer caused by passenger flow on international routes has not been addressed, especially in South America. This paper uses the Modified Fuel Percentage Method (MFPM) method and the ICAO standard method to calculate the CO 2 emissions of international routes in South America, containing the emissions of each route and each airline. Then we calculate the carbon emissions transfer and carbon compensations generated from South American international routes. In addition, this paper reasonably predicts the carbon emission data from 2023 to 2027 under three different scenarios (L&2027&100%, M&2026&100%, and H&2027&90%). We find that the five countries receiving the most considerable amount of carbon compensation are Argentina, Brazil, Peru, Colombia, and Chile, which is different from the ranking in 2019–2021. In addition, frequently using old and low-efficient aircraft is also a fundamental reason for excessive carbon emissions. Therefore, this paper provides reliable data support and reasonable emission reduction suggestions for the carbon emission control of South America's aviation industry. • Calculated carbon transfers and offsets for South American international routes. • Argentina, Brazil, Peru, Colombia, and Chile are the five countries receiving the most considerable amount of carbon compensation. • The "H&2027&90%" scenario is optimal for South American aviation. [ABSTRACT FROM AUTHOR]

With the impact of covid-19 and geopolitical instability, the price of energy and medical-related chemicals are increased. Acetone is used as the main material for the synthesis of important disinfectant isopropanol, which the price is influenced by covid-19 impact. The research on acetone purification, especially on the acetone refining section in the cumene hydroperoxide process is of great importance, which is high in energy cost but the most popular method for acetone production. This paper provided novel energy-saving acetone purification processes with heat integration, heat recovery, and molecular sieve adsorption, and made comprehensive research of different schemes applications with optimization. A modified TAC calculation model for adsorption units is first proposed in this paper, which includes the calculation of adsorption unit cost, molecular sieve loss, and molecular sieve regeneration cost. CO 2 emission and thermodynamic efficiency of all the processes are compared. A semi-quantitative analysis is used to compare the relative industrial versatility and process controllability. The results show that the combination of adsorption and heat integration achieves a decrease of 27.75% in energy consumption, 15.30% in TAC, 7.09% in CO 2 emission, and a 22.62% relative increase in thermodynamic efficiency. [Display omitted] • Comprehensive energy-saving research for novel acetone purification processes. • Cost estimation model proposed for adsorption units. • The multiple-scale analysis applied with quantitative/semi-quantitative comparison. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP. • A novel two-stage scheduling framework of VPP is proposed. • VPP consists of traditional DGs, ESSs, WPP, PV, EVs and controllable loads. • The scheduling approach reduces the impact of multiple uncertainties on VPP. • A profit distribution strategy of the VPP is proposed. [ABSTRACT FROM AUTHOR]

Increasing energy consumption rates and concerns over environmental problems like global warming have led researchers to provide new energy supply solutions, one of which is the use of combined energy systems. Combining conventional fossil energy cycles with renewable cycles is one of the most effective ways to improve their performance and also help harvest green energy. To address this solution, in this paper, a hybrid novel energy system including a gas cycle, a steam cycle, two organic Rankine cycles (ORC), and a renewable cycle based on geothermal and solar energy sources using concentrating photovoltaic thermal panels (CPVT) is proposed. A comprehensive study has been conducted on this system from the perspective of energy, efficiency, economy, and the environment. Different fluids have been investigated for use in ORC subsystems in this study. Additionally, a parametric study has been conducted to determine how the cycle's overall performance is affected. Multi-objective optimization using the Non-dominated Sorting Genetic Algorithm second version (NSGA-II) has been performed based on the objective variables of exergy efficiency and capital costs. In this paper, Engineering Equation Solver (EES) and MATLAB are used for modeling and optimization. The results show that the best fluids to use in ORC are R123 in the first cycle and Ammonia in the second cycle, which energy efficiency, exergy efficiency, exergy destruction rate, and generated power in the base state for the whole cycle are 50.59%, 25.44%, 1537.35 kW and 524.66 kW, respectively. Also, the amount of annual capital cost and the amount of carbon dioxide emission from energy and exergy are 107,034 dollars, 11,672, and 35,401 kg per month, respectively. The optimization results indicate a 0.25% improvement in exergy efficiency and a 500$ annual capital cost reduction at the optimal point. • Providing a new combined cycle with a combination of fossil and renewable energies. • Conducting energy, exergy, economic and environmental analyzes (4E). • Performance analysis and selection of the best operating fluids for ORC cycles. • A parametric study of factors affecting cycle efficiency and total cost. • Multi-objective optimization of the cycle using DoE, ANN, and NSGA-II methods. [ABSTRACT FROM AUTHOR]

The carbon market has been playing an increasingly important role in mitigating carbon emissions. However, with the tightening of the carbon allowance, where the carbon market will evolve is still unclear. Abstracting three agents—Government, Carbon-supplier('CS'), Carbon-buyer('CB'), this paper intends to investigate the operation mechanism of carbon market from the perspective of the stakeholders. Based on the simulation analysis, it is suggested that: 1) the carbon market in China may become a major "seller's market" when carbon quota experienced dramatic reduction; 2) when the carbon price is constant, CS with excessive carbon productivity will crowd out and exploit CB in the carbon market competition; 3) strict administrative penalties and information disclosure would help promote proactive participation of CS and CB in the carbon market; and 4) the interaction between CS and CB in different strategic scenarios is influenced by the carbon price and the coefficient of demand variation for carbon quota. In contrast with previous studies, this paper has uncovered a dynamic strategic evolution mechanisms of stakeholders in the carbon market at the micro-level, contributing to carbon market governance and providing stakeholders with tailor-made climate policy tools and strategic choices. • The carbon market in China may evolve into a "seller's market" with a scarcer allowance. • We first identify the phenomenon of carbon exploitation in the carbon market. • Improving the carbon price under buyer's market would facilitate positive participation of three agents. • Carbon accounting, carbon disclosure, and strict administrative penalty help regulate non-compliance actions by enterprises. • The equilibrium of agents' strategy are influenced by dual aspects—parameters and other stakeholders' choices. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) is considered as an effective means to improve efficiency and reduce carbon emissions. To further improve the efficiency and low-carbon benefits of IES, this paper proposes a novel low-carbon planning model for the electricity-gas-heat integrated energy system (EGHIES), in which a long-term, multi-stage planning approach is adopted for configuring energy production, conversion, storage equipment and transmission networks. This model mainly realizes the coupling and energy conversion of power, natural gas and heating systems through the combined heat and power (CHP). And the power-to-gas (P2G) model considering reaction heat is established and introduced into IES planning to deepen the coupling degree of the three subsystems, so as to improve the efficiency of P2G and the system. Considering it is difficult to effectively constrain carbon emissions with a fixed carbon trading price, this paper adopts a ladder-type carbon trading mechanism (LTCTM) to strengthen the effect on carbon emissions. To further improve the low-carbon benefits of IES, the carbon capture system (CCS) model installed in coal-fired generation (CG) and coal-fired boiler (CB) is established, and the CO 2 captured by CCS is supplied to P2G for gas production. Finally, the effectiveness of the proposed model is verified by simulation results. [Display omitted] • A long-term, multi-stage approach for the integrated energy system is proposed. • The energy production, conversion, storage equipment and networks are configured. • The power-to-gas model considering reaction heat is established. • The carbon emissions are constrained by the ladder-type carbon trading mechanism. • The low-carbon retrofit is considered in coal-fired equipment. [ABSTRACT FROM AUTHOR]

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers. • Carbon awareness based integrated optimization model for data center in China is proposed. • Layout, scale, cooling technology and power supply mix are simultaneously considered. • 25% of newly established data center electricity related emissions in China can be avoided. [ABSTRACT FROM AUTHOR]

In the configuration of distributed energy systems, the impact of ambient temperature on gas turbines is often overlooked, leading to inaccuracies and suboptimal performance. Previous research, which have used fixed operation strategies and electric cooling ratios, also leaves room for improvement. This paper addresses these issues by finely considering the mathematical model of micro gas turbines (MGT) in distributed energy systems with multiple energy storage (DES-MES), and by taking into account ambient temperature and operating power to determine the electrical efficiency of MGT throughout the year. This paper employs a two-layer co-optimization method to configure device capacity and find the optimal variable operation strategy and electrical cooling ratio. This study improves the annual total cost saving rate, carbon dioxide emissions saving rate, and primary energy consumption saving rate by 35.4%, 67.6%, and 64.7%, respectively, compared to the separate production system. By accounting for the effect of ambient temperature on MGT, the annual performance of DES-MES is improved by 24.38% compared to fixed electrical efficiency MGT model. Additionally, the annual performance of DES-MES using variable operation strategy and electric cooling ratio is optimized by 23.89% compared to fixed operation scheme. • Micro gas turbine model influenced by ambient temperature. • Integrated energy system with variable operation scheme. • A two-layer co-optimization approach considering both configuration and operation. • Combination of improved honey badger algorithm and Cplex solver. [ABSTRACT FROM AUTHOR]

Enhancing the frequency response ability of the wind-thermal coordinated load frequency control system is an effective means to maintain the stability of the grid frequency and improve the flexibility of the new power system. Thus, this paper designs a cascaded extended state observer sliding mode control (CE-SMC) method based on the osprey-dung beetle fusion optimization algorithm and applies it to the wind-thermal coordinated system to improve the frequency regulation performance of the system. Firstly, cascaded extended state observers are utilized to accurately estimate the combined effects of parameter uncertainties and internal and external disturbances in the system and compensated by sliding mode controller in real time, which helps the system to achieve fast frequency regulation. Secondly, the osprey-dung beetle fusion optimization algorithm is employed to optimize the parameters of CE-SMC. Moreover, a multi-objective function including the frequency regulation performance, thermal power unit output smoothness, carbon emission and economy is constructed to guide the power system to operate at the utopian point of flexibility, economy and low carbon. The control scheme effectively combines the features of fast frequency response, robust immunity and accurate parameter tuning. Finally, simulations are carried out in different load disturbances and various wind power penetration rates. Results show that the CE-SMC scheme can effectively improve the frequency response ability of the system while taking into account energy saving and emission reduction. Compared with integral sliding mode control, the frequency regulation performance is improved by 20.99 %, the output stability of thermal power unit is improved by 19.31 %, the power generation cost is reduced by 1.88 % and the carbon emission is reduced by 2.16 % in single load perturbation. As such, the CE-SMC algorithm provides a practical reference for the flexible, low-carbon and economic operation of new power system. • A practical controller combining cascaded extended state observer and sliding mode control is proposed. • A multi-objective function including frequency regulation ability and carbon emission is defined. • An osprey-dung beetle fusion optimizer is designed to solve multi-objective optimization problems. • The effectiveness of the strategy is verified under multiple load perturbation scenarios. [ABSTRACT FROM AUTHOR]

—Regarding the increase of carbon dioxide emissions with growing energy demand, recent emerging technologies for multi-energy system have attracted much attention, as they can effectively couple and coordinate different energies to further control carbon dioxide emissions. This paper focuses on distribution-level multi-energy system and proposes an optimal scheduling method for hydrogen support based on dual-agent energy-carbon price interactive response. Firstly, a hydrogen-supported multi-energy closed-loop flow mode is developed, which provides an effective way to improve renewable energy consumption and brings additional economic revenue of solid carbon. Secondly, a dual-agent energy-carbon price interactive response scheme is designed based on the concept of energy-carbon participation ratio, which takes carbon dioxide emissions of gas load and various carbon revenues into account. This scheme can motivate users for energy demand adjustment and active carbon dioxide reduction by setting energy and carbon prices as two guiding signals. Finally, a bi-level optimal scheduling model is proposed for dual agents of energy service provider and users to maximize the benefit of each side. Case study illustrates that the proposed model can effectively balance benefits of dual agents and has economic advantage of carbon dioxide reduction, while promoting the consumption of high proportion renewable energy and system stability. • Hydrogen-supported multi-energy closed-loop flow mode is developed. • DECIR scheme is designed for active energy adjustment and CO 2 reduction of users. • Bi-level scheduling model balancing both benefits of dual agents is proposed. • The proposed model improves the economy, low carbon and stability of MES. [ABSTRACT FROM AUTHOR]

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40 %. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36 %. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change. • We assess the potential effect of RCPs and GCMs on multiscale hydropower generation uncertainty in the UYRB from a new angle. • The contribution of multiple uncertainty sources to multiscale hydropower generation uncertainty is dynamically quantified. • Climate and hydrological models, emission scenarios and their interactions greatly impact hydropower generation uncertainty. • Interaction effects of multiple uncertainty sources on the uncertainty increase before, after and during the non-flood season. [ABSTRACT FROM AUTHOR]

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO 2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions. • The peak shaving models of once-through boiler under full operating conditions are presented. • Large-scale unknown parameters of boiler models are innovatively optimized by RL algorithm. • Multi-objective MPC is constructed for plant to meet various peak shaving requirements. • A dry/wet state automatic conversion control scheme is proposed for CHP plant. • The flexible and deep peak shaving capacity of one-through boiler is promoted. [ABSTRACT FROM AUTHOR]

Direct-fired supercritical CO 2 (sCO 2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents detailed techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO 2 power cycle, which is lacking in public literature. The study considered multiple plant configurations with varying levels of thermal integration with the plant air separation unit (ASU) to shows the systematic impact of thermal integration on plant performance and economics. Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE). The optimized direct sCO 2 power plants achieved ∼48 % plant efficiency (HHV basis) which is similar or slightly higher plant efficiencies than state-of-the-art NGCC plants based on the F-class gas turbine with carbon capture and storage (CCS). The LCOE of the optimized direct sCO 2 plants is 13–17 % higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO 2 power block, though there is significant room for improvement due to high uncertainty in component capital costs for these new plants. Based on the results, direct sCO 2 power cycles have the potential to achieve >50 % efficiency (HHV basis) with >98 % CO 2 capture rate which can lead to significant efficiency improvement and CO 2 emissions reductions compared to NGCC plants with CCS. • The paper presented the techno-economic optimization of natural gas-fired utility-scale power plants based on direct sCO 2 power cycles. • Optimization considered a wide range of design parameters and was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms. • Optimized direct sCO 2 plant offered 0.7 pp higher plant efficiency but 13.5% higher LCOE compared to the state-of-the-art NGCC plant with CCS. • Based on the uncertainties, additional sensitivity analyses indicate that 50 % reduction in ASU CAPEX will decrease plant LCOE by ∼10 % making the technology cost competitive with NGCC plants with CCS. • This study presents the clearest picture of the cost and performance potential for direct sCO2 power cycles in public literature and identifies areas of R&D that could expedite the commercialization of this technology. [ABSTRACT FROM AUTHOR]

Currently, the worldwide implementation of the Montreal Protocol has accelerated the development of low GWP refrigerants. R290 as an alternative refrigerant with low GWP has great advantages over R134a for heat pump applications. However, the use of expansion valve throttling in the heat pump cycle has a large irreversible loss. Therefore, an ejector-enhanced sub-cooler vapor injection heat pump cycle (ESVIC) with R290 for water heating is proposed in this paper to improve the energy efficiency by applying ejector. The thermodynamic models are established based on the 4E (energy, exergy, economic and environmental) analysis to compare its performance with the basic ejector-enhanced heat pump cycle (BEEC) and the sub-cooler vapor injection heat pump cycle (SVIC). The energy analysis results show that compared with the BEEC and SVIC, the volumetric heating capacity of the ESVIC is improved by 42.0 % and 21.2 %, and the heating coefficient of performance (COP h) of the ESVIC is improved by 27.6 % and 5.3 %, respectively. The exergy analysis results indicate that in BEEC, SVIC and ESVIC the exergy destruction of the expansion valves is 0.3 %, 13.5 % and 0.8 %, respectively. It is indicated that the adoption of ejector can significantly decrease the exergy destruction of expansion valves. Moreover, the environmental analysis results reveal that the carbon emissions of ESVIC is 21.5 % and 5.0 % lower than those of BEEC and SVIC, respectively. Besides, for the ESVIC system, the carbon emissions of the life cycle climate performance when using R290 are 9.9 % lower than that of using R134a, indicating that the R290 is a potential substitute for R134a in heat pump applications. The economic analysis results demonstrate that the unit exergy production costs of ESVIC is 21.6 % and 5.1 % lower than BEEC and SVIC, respectively. • An ejector-enhanced R290 heat pump cycle with a sub-cooler is proposed. • Energy, exergy, economic and environmental analysis models are established. • The impact of key operation parameters on the cycle performance is discussed. • The 4E performance of the ESVIC is batter than the BEEC and the SVIC. [ABSTRACT FROM AUTHOR]

Electricity market-oriented reform and carbon emissions trading, as the main policies implemented in China's power sector, have a profound impact on China's green and low-carbon transition by promoting resource allocation efficiency and internalizing external environmental costs, respectively. Therefore, this paper constructed a dynamic recursive CGE model to measure the energy, economic, and environmental impacts of carbon emissions trading and electricity market-oriented reform from electricity market structure and pricing mechanism. The results show that the two policies have a synergistic effect on industrial structure optimization, energy consumption structure optimization, and carbon emission reduction. Specifically, the share of value-added of the tertiary sector, the growth rate of coal consumption, the share of thermal power consumption, and carbon emissions in 2030 are reduced by 5.70 %, 18.29 %, 2.55 %, and 2.838 trillion tons, respectively, compared with the scenario without carbon emissions trading and electricity market-oriented reform. However, imperfectly competitive market structures outperform perfectly competitive market structures in energy consumption structure and carbon emission reduction. Electricity price regulation is also more beneficial to GDP growth and increase in residential disposable income compared to price liberalization. To this end, returning emission trading scheme revenues to residents can effectively mitigate the adverse impacts caused by market-oriented reforms. • EMR is involved in market structure, pricing mechanisms, and market regulation. • EMR and CET have a synergistic effect on carbon reduction. • Energy consumption structure is better in imperfectly competitive market. • Electricity price regulation is more beneficial to GDP growth. • Returning ETS revenue to residents increases per capita disposable income. [ABSTRACT FROM AUTHOR]

The Joule cycle Linear Engine Generator (LEG) is a promising power generation technology with the potential to achieve zero carbon emissions. However, the LEG expander valve actuation system presents unique challenges due to its lack of a traditional crankshaft, the need for swift valve lift and reversal, and variable lift. This paper presents a Linear Variable Valve Actuation (LVVA) system for a LEG prototype. The LVVA system is powered by voice coil motors. Rigorous experimental investigations were conducted to analyze crucial performance factors, including energy consumption, force balance, energy flow distribution, and the relationship between valve lift duration and energy consumption. The results show that the LVVA system can achieve the desired valve lift and timing, as well as very small variations in LEG performance compared to the model using an ideal lift curve. The LVVA accounts for approximately 3.59 % of the LEG power output. The energy consumption of 1.607 J per valve stroke provides a slight advantage over traditional actuation systems. The obtained optimal lift curves were used to refine the LEG model. The influence of valve lift curves on LEG performance was evaluated which reveals rapid valve openings and relatively short duration contributing to improved LEG performance. [Display omitted] • Valve actuation validation contributes to a 1D Linear Engine model. • The valve actuation system consumes about 1.607 J per valve lift cycle. • Valve openings durations improves energy consumption of valves and engine performance. • The valve actuation energy demands 3.59 % of Linear Engine's power output. [ABSTRACT FROM AUTHOR]

Effectively utilizing low-grade thermal energy is a promising approach to mitigating greenhouse gas emissions while reducing the burden on centralized power grids. Current thermoacoustic heat pumps and power generators face challenges such as high onset temperature differentials and low performance. This paper addresses these challenges by introducing a gas-liquid resonator into a thermoacoustic combined heat and power systems to recover low-grade thermal energy in residential buildings. Through Sage modeling and calculations, the internal characteristics of the proposed system and its output performance under different operating conditions are explored. At a heating temperature of 350 °C, the system can generate 6.4 kW of output thermal power, 0.9 kW of electricity, and overall exergy efficiency is 79.3 %. Combining neural network models with case studies conducted in Spain and Finland, the system can annually save 5.6 MWh and 20.7 MWh in fuel energy, reduce emissions of 1374 kg and 5180 kg of carbon dioxide, and save a total cost of €611 and €2324, respectively. Furthermore, comparisons with other emerging micro-CHP systems highlight the efficiency of the proposed system. These results indicate the high potential of thermoacoustic combined heat and power systems in recovering low-grade thermal energy and achieving energy savings and emission reductions. • Introducing an innovative thermoacoustic micro-CHP system. • Achieving an exergy efficiency of 79.3 % under standard operating conditions. • One system can annually reduce household CO 2 emissions by 5180 kg. • Ideal for households, balancing lower output levels for both heat and electricity. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEV) have become an important driving force for carbon reduction in the transportation industry. This paper adopts the extended environmental pressure assessment (STIRPAT) model and the time-varying difference in differences (DID) model to evaluate the effectiveness of the NEV subsidy policy to achieve sustainable governance. The results show that the NEV subsidy policy can significantly decrease the carbon emission of the transport industry. The mechanism analysis results indicate that the NEV subsidy policy can decrease carbon emissions by promoting a green transformation of the consumer car purchasing structure. Further analysis found that a "fraudulent subsidy" can significantly weaken the policy effect of subsidies. The results of heterogeneity analysis indicate that the subsidy policies introduced by different levels of government have different effects. The subsidy policy of the central government has a more significant effect, and repeated subsidies from central and local governments will weaken the policy's effectiveness. These findings provide valuable insights for policymakers in developing countries to optimize subsidies for new energy vehicles in reducing carbon emissions in the transportation industry. • We explore the impact of the NEV subsidy policy on carbon emissions of the urban transport industry in Chinese cities. • The NEV subsidy policy significantly reduces the carbon emissions of the urban transport industry. • The NEV subsidy policy can promote a green transformation of the consumer car purchasing structure. • The fraud of government subsidies will weaken the carbon reduction effect of the NEV subsidy policy. • The impact of NEV subsidy policy from central and local governments on urban carbon reduction may vary. [ABSTRACT FROM AUTHOR]

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs. Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports. • LP models optimized outbound logistics networks with multiple modes of transport. • Minimized CO 2 emissions and total costs including transportation and carbon costs. • Modelling multimodal distribution networks including road, water, rail and dry port. • Used real data and supported strategic decision making for a Thai sugar company. • Provides a comprehensive review of LP models for optimizing distribution network. [ABSTRACT FROM AUTHOR]

Based on Green Slow model, this paper builds a framework to achieve carbon peak driven by manufacturing, energy-conservation and energy substitution technology concurrently.Through a panel dataset from 2000 to 2018 at the province level, we find that the increasing carbon emissions effects from manufacturing technology are primarily due to increased levels of disposable income resulting from technological advances applied in production, which increases the demand for energy consumption and results in the emission of more carbon dioxide. China's carbon emissions have been significantly reduced by energy-conservation and energy-substitution technology, which reduce the amount of fossil energy used in the generation of clean energy while increasing its share. These results suggest that to achieve carbon peak and further improve the carbon reduction effect of energy-conservation and fossil fuel alternatives technologies, local governments should strive to create an enabling environment for technology development. • Theoretical analysis for achieving carbon peak is conducted. • Manufacturing technology drives carbon emission. • Enlarging the production scale is the reason contributes to the increased carbon emissions. • Energy conservation and substitution technology mainly cause this emissions reduction. [ABSTRACT FROM AUTHOR]

Integrating solar heat and calcium looping (CaL) process into the existing lignite-fired power generation system is a promising technology to control CO 2 emission. However, the heat released from carbonation process led to the temperature increase of exhaust flue gas together with heat losses in boiler. To recover the waste heat from the exhaust flue gas, this paper proposed a solar-aided lignite-fired power generation system with CaL process CO 2 capture, lignite pre-drying and feedwater preheating (i.e., design III). Besides, design I without waste heat recovery and design II with only lignite pre-drying were simulated for comparison. Through the case study based on a solar-aided 330 MW lignite-fired power generation system, thermal and techno-economic performance of design III was the best on the design condition. Its standard coal consumption rate was 374.44 g/kWh, which was 61.12 g/kWh and 8.24 g/kWh lower than design I and design II, respectively. With the new design, the annual net profit of design III is $18.2924 M, with $0.5652 M higher than design II. The sensitivity analysis indicated that the thermal performance of design III was also the best under various conditions of CO 2 capture efficiency and integrated solar heat. • Solar tower, lignite Pre-drying and Feedwater preheating were integrated with CaL process CO 2 capture. • The new design recovered the waste heat from exhaust CO 2 -lean flue gas caused by carbonation process. • The new design decreased the standard coal consumption rate and increased the thermal efficiency. • The new design was proved economically feasible based on annual net profit. • Sensitivity analysis was conducted to study the influences of CO 2 capture efficiency and integrated solar heat. [ABSTRACT FROM AUTHOR]

Considering the importance of efficient thermal integration for a gas turbine cycle using its energetic flue gas, the current paper proposes an eco-friendly and efficient process integrated into a gas turbine cycle. The newly designed polygeneration structure produces power, hot water, and methanol. The components of this system include a heat provider unit, a direct carbon dioxide hydrogenation unit for the production of methanol, an ammonia Rankine cycle, a proton exchange membrane electrolyzer, a supercritical carbon dioxide power cycle, and two organic Rankine cycles. The feasibility of the proposed model is evaluated using a comprehensive multi-variable assessment from energy, exergy, economic, and environmental approaches. This system exhibits a power production capacity of 17197.23 kW and a methanol generation rate of 0.7573 kg/s. Also, the total irreversibility is equal to 68161.18 kW. Besides, the total energy and exergy efficiencies are found as 68.73% and 32.03%, respectively. This model's carbon dioxide emission is 18667.36 kg CO2 /h, and its carbon footprint equals 0.2196 kg CO2 /kWh. Furthermore, the economic analysis reveals that the project has an annual total cost of 194,602,338 and a net present value of 686,221,616. Besides, the computed total unit costs for products and energy are 0.3385 $/kWh and 89.81 $/GJ, respectively. [Display omitted] • New heat integration combined with a gas turbine, producing power, heat, and methanol. • Comprehensive multi-variable approach for evaluating the feasibility of the model. • Aspen HYSYS software is used for simulation as well as sensitivity study. • Exergy efficiency and CO 2 footprint are 32.03% and 0.2196 kg CO2 /kWh. • Total unit cost of products and net present value are 89.81 $/GJ and $686,221,616 [ABSTRACT FROM AUTHOR]

Thermochemical energy storage (TCES) is a promising technology to support the world's initiatives to reduce CO 2 emissions and limit global warming. In this paper, we have synthesized and characterized a new three-component composite materials consisting of a mixture of calcium chloride and iron powder confined inside the expanded vermiculite. The new approaches of studying composite sorbents of ammonia using a gas flow-through microcalorimetry proposed in this work. The energetics of adsorption as a function of ammonia uptake was measured at room temperature (RT), 106 and 150 °C. The enthalpy of NH 3 sorption in eight cycles tested ranged from 12.2 to 39.1 kJ mol−1. The strength of ammonia sorption on composite surface was characterized by TPD (Temperature Programmed Desorption). Based on the NH 3 -TPD profiles of composites it was found that the high-temperature desorption peaks of vermiculite sample shifted to lower temperature after the deposition of salt. The characterization of the composites was complemented by the laboratory analyses using XRD, WD-XRF, FTIR, TG/DTG, SEM-EDS and nitrogen sorption isotherms at −196 °C (BET method). The composite impregnated with 37 wt% of salt has the highest enthalpy and sorption capacity, thus seems to be the most promising candidates for the heat storage systems. • The potential of vermiculite-CaCl 2 composite modified with iron powder for TCES was studied. • The average enthalpy of irreversible sorption of NH 3 on composites at temperature range of RT to 150 °C were measured to be 31 kJ/mol. • NH 3 -desorption peaks of composites occurred with a distribution significantly shifted towards lower temperatures <250 °C compare to vermiculite. [ABSTRACT FROM AUTHOR]