Emission Reduction Goals Research Articles

What Is the Effect of China’s Renewable Energy Market-Based Coupling Policy?—A System Dynamics Analysis Based on the Coupling of Electricity Market, Green Certificate Market and Carbon Market

In the context of China’s electricity market reform, green certificate trading and carbon trading, as important policy tools to promote the development of renewable energy and energy conservation and emission reduction in the power industry, will inevitably be coupled with the electricity market. In order to study whether the coupled market can successfully achieve the goals of power supply structure adjustment and carbon emission reduction, this paper establishes a system dynamics (SD) model, analyzes the correlation and coordination mechanism among the green certificate market (TGC), carbon market (ET) and electricity market, including generation right trading, and simulates the changes of market price and power supply structure. The results show that (1) the power price under the coupling of three markets includes the TGC price and the ET price, so it is influenced by the ratio of renewable portfolio standards (RPS) and carbon reduction policy; (2) the combination of the TGC mechanism and the ET mechanism will be conducive to the optimization of long-term market power supply structure, so as to promote the realization of emission reduction targets; and (3) power generation rights trading, as a carbon reduction policy, will reduce the power generation of fossil energy in the short-term market, but in the long run, it will lead to the loss of momentum for the development of renewable energy. Therefore, regulators need to reasonably adjust different policies in order to give full play to the comprehensive regulatory role and help the energy and power industry and the low-carbon transformation of society.

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals.

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.

Traversing the Jevons Paradox: The dual role of residential photovoltaic systems in electricity conservation and increased consumption in an emerging economy

In response to escalating global climate change, governments and international organizations were engaged in identifying effective strategies to mitigate carbon emissions. Residential photovoltaic systems emerged as a prominent, clean energy solution, drawing attention for their carbon-neutral electricity generation. Despite this, concerns persisted over whether technological advancements and policy incentives could sustain efforts to conserve energy and reduce emissions, particularly in light of the Jevons Paradox, which posits that technological progress might increase energy consumption and counteract emission reductions. This study, grounded in self-identity theory, examined the influence of residential PV installation on household electricity conservation behavior and assessed the paradox's relevance in this context. Through a survey across three representative Chinese provinces, collecting 996 responses, the research utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data. Findings revealed that PV installation and usage among residents bolstered self-consistency, social identity, and identity affirmation, indirectly encouraging electricity conservation. Economic incentives and environmental campaigns were both shown to increase residential PV adoption and subsequent electricity-saving actions. However, economic incentives were found to be more effective, suggesting that economic benefits primarily influenced residents' energy consumption behaviors. Interestingly, a lower inclination for electricity conservation among higher-income residents hinted at the Jevons paradox (or a rebound effect). The study's findings underscore the importance of crafting energy and environmental policies that balance economic incentives with long-term environmental awareness strategies. It also suggests that reinforcing residents' identities as environmental advocates could enhance engagement in green energy practices, offering valuable insights for China and other nations striving to promote residential PV systems and meet emission reduction goals.

Using STPA for hazard identification and comparison of hybrid power and propulsion systems at an early design stage

The decarbonization of the shipping industry is a hot topic and new emission reduction goals, regulations, and initiatives all over the world underline its importance. These goals apply to the ocean going fleet, as well as coastal shipping and fisheries. Both academia and industry, work on the development and testing of new technologies to reach emission reduction targets. Besides technical feasibility and economic viability, the safety of new power and propulsion systems must be considered in this process. In this paper, the Systems Theoretic Process Analysis (STPA) method is used for hazard identification and comparison of two alternative hybrid power and propulsion systems for a small-scall fishing vessel. The assessment method allows for identifying focus areas for hazard reduction in a systematic manner. System safety requirements derived from the assessment can be considered when designing power and propulsion systems. Based on the results, a discussion of the method and the usefulness of the results is given. The main findings are that the assessment provides valuable insights into the hazards introduced by new power and propulsion systems in a qualitative manner. For a quantification of the risk, further work and an extension of the assessment are needed. The STPA results show that especially the control of the power sources and their interactions internally and externally with the consumer side are crucial. Therefore, special attention should be given to the design of the automated control system, feedback system, and user interaction with the system.

Design of Integrated Energy–Water Systems Using Automated Targeting Modeling Considering the Energy–Water–Carbon Nexus

The swift expansion of the global population and economy has spurred growing requirements for energy and water in recent decades. Inefficient energy and water consumption, however, has led to an increase in CO2 emissions. Hence, the socio-economic development of a country must consider the interconnections between energy, water and carbon, as there are mutual dependencies among these three elements. This work considers the nexus between energy, water and carbon in the design of integrated energy–water systems using a new automated targeting modeling (ATM) framework. ATM incorporates the advantages of the insight-based Pinch method and a mathematical programming approach to provide visual understanding for a thorough analysis of the problem while guaranteeing accurate solutions. Minimum targets of power and water based on the integrated network operation were established by the ATM, with corresponding carbon emissions. A specific goal of annual carbon emissions reduction was set as the constraint and the ATM optimized the capacities of the components in the system accordingly to achieve minimum overall cost. The application of ATM on an industrial plant case study shows that a target of 45% reduction in the carbon discharge amount was achieved by shifting to greener fuel in the energy system at a minimum overall cost increase of 0.45% only. The framework can assist users in meeting power and water loads in their plant while planning for the appropriate decarbonization efforts at the minimum possible cost.

Energy transition pathways towards carbon neutrality: the 2035 Apulia case

Abstract Changing how society perceives and uses energy is essential, and a change in the way energy is produced is also needed. Europe is striving to become the first climate neutral continent in 2050: therefore, it is crucial to draw a pathway to achieve this result. In this paper different energy scenarios for 2035 for a southern Italian region are analysed. As the major goal, carbon emissions reduction must be at least half of the current, considering 2035 as a halfway point between current situation and carbon neutrality goal set for 2050. The aim of this work is to analyse the role of a major increase in energy production from either photovoltaics or wind, to achieve which option is the best to be chosen. Photovoltaics and wind are green but also intermittent energy technologies. Therefore, it is beyond doubt that the uncontrollable nature of sun and wind sources requires to store energy. Doubling the total energy production from wind and photovoltaics, results show different conditions in terms of energy storage required and investment and operation costs. The methodology used is numerical simulation of the Apulia case through EnergyPLAN software. Best conditions are achieved for higher energy production rate from wind, compared to current values. 50% emissions reduction goal is achieved for all analysed scenarios.

Analysis of the Potential Carbon Emission Reduction Through Interaction Between New Energy Vehicles and Smart Grids

Abstract: This paper analyzes the potential for carbon emission reduction through the interaction between new energy vehicles and smart grids, discussing how the optimization of energy efficiency and balancing of grid loads can be achieved through Vehicle-to-Grid (V2G) technology. The interaction between new energy vehicles and smart grids can enhance energy utilization efficiency, optimize grid management, and reduce reliance on fossil fuels by leveraging renewable energy sources, thereby achieving the goal of carbon emission reduction. However, this process faces multiple challenges including technological integration, cost-effectiveness, policy support, and societal acceptance. The paper proposes a series of strategic recommendations, including promoting technological standardization, enhancing policy and economic incentives, and increasing public awareness and participation, to support the effective interaction between new energy vehicles and smart grids, and to achieve long-term carbon emission reduction goals.

Recovering Low-Grade Heat from Flue Gas in a Coal-Fired Thermal Power Unit

To achieve the goals of carbon peaking and carbon neutrality, the retrofitting of existing coal-fired power plants is crucial to achieving energy-saving and emission reduction goals. A conventional recovery system of waste heat typically occurs downstream of the air preheater, where the energy quality in flue gas is low, resulting in limited coal-saving benefits. This study proposes a scheme involving a flue gas exchanger bypassing the air preheater and low-temperature economizers, which is used to transfer the waste heat from flue gas to primary and secondary air (System I). Additionally, a heat pump can be introduced to provide supplementary energy for primary and secondary air, as well as the condensate from the steam turbine (System II). The coal consumption rate and exergy efficiency are used to evaluate the two schemes. The results show that both waste heat recovery systems can increase the power output of the coal-fired unit by recovering waste heat. System II can boost power output by approximately 13.98 MW. The power increase in both waste heat recovery systems show a declining trend as the unit load decreases. This increased power is primarily attributed to the medium- and low-pressure cylinders, while the contributions from ultra-high-pressure and high-pressure cylinders are negligible. The increased power output for the medium-pressure cylinder ranges from approximately 3.49 to 3.58 MW, while the low-pressure cylinder has an increased power output of around 10.10 to 10.19 MW. The coal consumption rate is decreased from 250.3 g/(kW·h) to 247.5 g/(kW·h) under a full load condition for both systems, which can be augmented at lower load conditions. System II outperforms System I at 30% load condition, achieving a reduced coal consumption rate of 3.36 g/(kW·h). System I has an exergy efficiency of 40%, while System II shows a higher efficiency of 44%.

Achieving collaborative pollutant and carbon emissions reduction through digital governance: Evidence from Chinese enterprises

Under China's dual carbon goals, it is imperative for enterprises to effectively reduce carbon emissions while achieving pollutant reduction. This article explores the potential role of government-implemented digital governance in facilitating these objectives. Utilizing a quasi-natural experiment on digital governance, we established an analytical framework to thoroughly examine how digital governance can help Chinese enterprises realize synergistic effects in pollution control and carbon reduction. Our study reveals that digital governance significantly reduces enterprises' emissions of carbon dioxide and pollutants, with emission intensities of the carbon dioxide, air pollutant and water pollutant being decreased by approximately 6.9%, 7.3%, and 7.0%, respectively. These reductions are primarily driven by improvements in energy efficiency, the promotion of green innovation, digital transformation, and enhanced role of media. However, it is important to note that digital governance also negatively impacts overall corporate productivity and widens the digital divide between large and small-to-medium enterprises. The research results have practical significance for promoting the achievement of emission reduction goals and sustainable development for enterprises.

Experimental study of boiling critical heat flux with low mass flux under motion condition in a wetted perimeter equivalent rod

The significance of floating nuclear power plant has recently surged as a viable solution for addressing ocean energy supply issues, while also meeting energy conservation and emission reduction goals. During the initial phase of nuclear reactor startup or in the event of a LOCA (Loss of Coolant Accident), the occurrence of critical heat flux (CHF) at exceptionally low mass flux in the core of floating nuclear power plant is a possibility. This underscores the importance of CHF safety characteristics of floating nuclear power plant. Despite this, there has been a noticeable absence of published experimental research on CHF under motion conditions with high operational parameters. To bridge this gap, CHF experiments were carried out on rod test sections under both static and motion conditions, employing the wetted perimeter equivalent design method. This study scrutinized the impact of system variables on CHF and evaluated the effectiveness of the design strategy based on the wetted perimeter equivalent method, using experimental data as a reference. Additionally, it elucidated the CHF mechanism and identified the penalty factor on CHF under conditions of inclination, rolling, and heaving, with the maximum penalty factor reaching up to 15%. The insights gained from this research are instrumental in guiding the CHF test scheme design under motion conditions and lay a foundational basis for the thermal design of floating nuclear power plant.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Mapping the Global Carbon Emissions of Marine Sectors.

The ocean serves as a vital ecosystem for sustaining life on earth and ensuring human well-being. Presently, there is a significant surge in global demand for various ocean-based economic activities, including fishing, shipping, offshore wind energy production, maritime tourism, and so on. However, this growth has also resulted in an increase in emissions from marine sectors, which have not been thoroughly evaluated or analyzed. It is therefore necessary to conduct comprehensive evaluations of the current emissions, covering marine sectors. To address this need, through this Perspective, we have globally analyzed and discussed carbon emissions linked to maritime transportation, marine capture fisheries, marine aquaculture, offshore wind, ocean renewables, and crude oil production. Additionally, we explored country-specific scales for these emissions and discussed points for future research to address the existing gaps. By gaining a better understanding of emissions over the oceans, policymakers could prioritize policy measures for achieving emission reduction goals and promote sustainable ocean development.

Fit for 2030? Possible scenarios of road transport demand, energy consumption and greenhouse gas emissions for Italy

Decarbonizing the transportation sector is one of the key challenges to reduce greenhouse gas (GHG) emissions and meet EU Green deal 2030 targets. Road transport decarbonization requires a holistic approach encompassing various strategies and interventions aimed at fostering the sustainable transition of this sector. This challenge is crucial given that road is responsible for over 90% of the overall transport GHG emissions.Within this framework, this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU “fit for 55” target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios. Due to the current “deep uncertainty” regarding key economic and social variables in this period, two forecasting scenarios – named “moderate decarbo” and “high decarbo” – were designed and based on different and somewhat opposite hypotheses on exogenous variables (e.g., GDP) and penetration rates of measures and policies promoting sustainable transportation (e.g., subsidies for electric vehicles) already in place. Although the current European legislation refers only to Tank-to-Wheel (TtW) objectives, this study included also the Well-to-Wheel (WtW) evaluation, which is more accurate in assessing the GHG impact of different policies, energy carriers, and technologies. As for the past, results suggest that GHG reductions over the period 2005–2022 are mostly due to crises and stagnation of the Italian economy. “Moderate decarbo” forecasts for 2030 lead to a 12% reduction of TtW (and 15% for WtW) GHG emissions compared to 2005 levels, with ASI policies compensating for the assumed economic growth. In any case these values are far from the EU “Fit for 55” goal setting a 43.7% reduction of TtW emissions. Even under very favourable hypotheses (“high decarbo” scenario), GHG emissions might be reduced by only 28% for TtW and 33% for WtW, still well below EU objectives. Furthermore, passengers and freight show very different behaviour, with the latter contributing to emissions more than proportionally, and “shift-to-rail” policies, although very useful in some market segments, contribute moderately at the overall national level. The main conclusion of this study is that the pathway towards road transport decarbonization in Italy (and possibly in other EU Countries) is very uncertain, and the ambitious 2030 target is unlikely to be met with currently planned policies alone. The transportation system needs to be constantly monitored, and new policies have to be implemented to reach higher emissions reduction goals.

Strategic approach to accelerating regional bioenergy development: Bioelectricity for emission reduction and sustainability

The western Himalayas hold significant potential for generating bioelectricity from dry pine needle biomass. This approach provides multiple benefits, including forest fire prevention, electricity generation, and emission reduction. However, despite these advantages, the growth of this sector has fallen short of expectations. The projected electricity generation potential from pine needle biomass in this region could lead to a significant annual emission reduction of 1.7 MtCO2e through forest fire prevention and an additional 0.49 MtCO2e by displacing carbon-intensive grid electricity. Regional bioenergy development plays a crucial role in the global energy transition and aligns with SDG 13, "Climate Action," and SDG 7, "Affordable and Clean Energy." By focusing on bioenergy as a renewable and accessible energy source, local and regional communities can contribute to climate action while simultaneously ensuring affordable and clean energy for their communities. Therefore, this paper employs a hybrid SWOT-AHP analysis as a strategic planning tool to achieve emission reduction targets by stimulating regional bioenergy growth. Twenty-nine SWOT factors were identified under four variables, and then the AHP technique was employed to assign priority weights to both SWOT variables and factors. Combining SWOT with AHP analysis provides quantitatively determined priorities for the factors, enabling their objective comparison. Eventually, this study offers a comprehensive perspective that leads to policy recommendations and serves as a valuable resource for relevant stakeholders, policymakers, and researchers seeking to achieve emission reduction goals through bioelectricity generation.

Design analysis and techno-economic assessment of a photovoltaic-fed electric vehicle charging station at Dhaka-Mawa expressway in Bangladesh

Electric vehicles are crucial for sustainable transport and energy solutions, particularly in developing countries like Bangladesh where their popularity is rising. This study primarily focuses on the techno-economic design of a 300 kWp solar photovoltaic-powered electric vehicle charging station along the Dhaka-Mawa Expressway in Bangladesh, capable of charging 20 electric vehicles simultaneously. The design utilizes the commercially available software package PVsyst 7.2 to ensure the feasibility and efficiency of the charging infrastructure. The use of solar photovoltaics for electric vehicle charging, compared to traditional grid-based methods, offers substantial environmental benefits, including significant reductions in carbon emissions. This shift is driven by decreasing costs, improved module efficiencies, and increased environmental awareness. The estimated levelized cost of energy is calculated at 7.1556 BDT/kWh (BDT is Bangladeshi Taka), with an annual energy generation cost of 1436285.32 BDT. Over a projected lifespan of 25 years, the system is expected to replace 8065 tons of CO2 emissions with its own emissions totaling 537.56 tons, resulting in a net decrease of 6460.2 tons of CO2. This approach not only aligns with Bangladesh’s emission reduction goals but also exemplifies the potential for solar photovoltaic systems to enhance sustainability in transportation. It also emphasizes the importance of integrating renewable energy sources into the electric vehicle-based infrastructure to achieve true sustainability and supports the country’s commitment to combating climate change through technological innovation.

Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges

AbstractGlobal climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long‐term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large‐ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy‐related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno‐economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large‐ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large‐ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.

Sustainable manufacturing process thermal energy environment detection and green economy development based on image classification

With a growing global focus on sustainability, manufacturing needs to ensure productivity while reducing resource consumption and environmental impact. This paper discusses the thermal energy environment detection method based on image classification in order to improve the energy efficiency monitoring in the manufacturing process and promote the development of green economy. In this study, deep learning technology was used to train convolutional neural network (CNN) to classify thermal environment data, collect thermal data images from multiple manufacturing processes, and label them. The pretreatment technology is used to improve the data quality, and the classification effect is optimized through multi-level network structure. The training and verification process of the model ensures its reliability in practical application scenarios. The experimental results show that the proposed image classification method achieves high accuracy in thermal environment detection, which is obviously superior to the traditional method. Through real-time monitoring of thermal energy status, timely identification of abnormal conditions, so as to provide effective data support for the manufacturing process, and promote the realization of energy conservation and emission reduction goals. Therefore, the thermal environment detection based on image classification provides a new technical path for the sustainable development of the manufacturing industry, and helps enterprises realize the transformation and upgrading of the green economy.

Does administrative division adjustment reduce air pollution? Evidence from city-county mergers in China

Administrative division adjustment plays a crucial role in transforming the spatial layout of cities and effectively contributes to green transformation and high-quality regional economic development. We empirically investigate the impact of administrative division adjustment, exemplified by city-county mergers (CCM), on air pollution using the difference-in-differences method. Our results demonstrate that CCM significantly reduces air pollution, which passes a series of robustness tests including heterogeneous treatment effect tests. Mechanism analysis reveals that CCM reduces air pollution by optimizing industrial structure, promoting market allocation, and constraining government size; geo-climatic conditions significantly moderate the haze reduction effect of CCM. Further analyses indicate that the haze reduction effect of CCM is more pronounced in the eastern and western regions, the southeast regions of the Hu Line, and non-resource-based regions. Moreover, CCM not only alleviates air pollution in border areas and mitigates the pollution boundary effect but also yields the social welfare effect. This paper provides valuable insights for maximizing the haze reduction potential of administrative division adjustment, understanding the role of Chinese-style decentralization in regional air pollution management, especially in border areas, and supporting the achievement of carbon emission reduction and sustainable development goals.

Integrated batch production planning and scheduling with machine-learning based production capacity region considering wind energy system

Continued greenhouse gas emission has led to increased global warming. Towards the goal of carbon emissions reduction and environmental sustainability, replacing high-polluting fossil fuel by clean energy has become a top priority in the decision-making of the current chemical industry. This work presents an optimization framework to address the integration of batch chemical production planning and scheduling considering energy consumption, which is a vital operational problem in chemical production. Assisting the linkage between planning layer and scheduling layer, a novel linear support vector machine based production capacity region calculation method is proposed to identify feasible regions of the scheduling layer. Uncertain factors in both the production system and the energy system are considered, including demand volatility of chemical products, energy supply fluctuations and time-varying energy market prices. To evaluate each strategy and the proposed model, simulation experiments are performed in three representative case studies. The results reveal that the integrated model considering energy consumption shows superior performance in different energy configurations, with or without wind energy generation and battery storage systems.